(2019-07-17) Chief Technical Officer (CTO) at ELDA
Chief Technical Officer (CTO)
Under the supervision of the CEO, the responsibilities of the Chief Technical Officer (CTO) include planning and supervising technical development of tools, software components or applications for language resource production and management. He/she will be in charge of managing the current language resources production workflows and co-ordinating ELDA?s participation in R&D projects while being also hands-on whenever required by the language resource production and management team. He/she will liaise with external partners at all phases of the projects (submission to calls for proposals, building and management of project teams) within the framework of international, publicly- or privately-funded projects.
This yields excellent opportunities for creative and motivated candidates wishing to participate actively to the Language Engineering field.
Profile: ? PhD in Computer Science, Natural Language Processing, or equivalent ? Experience in Natural Language Processing (speech processing, data mining, machine translation, etc.) ? Familiarity with open source and free software ? Knowledge of a statically typed functional programming language (OCaml preferred) is a plus ? Good level in English, with strong writing and documentation skills in English ? Dynamic and communicative, flexible to work on different tasks in parallel ? Ability to work independently and as part of a multidisciplinary team ? Citizenship (or residency papers) of a European Union country ? Good level in Python, knowledge of Django would be a plus ? Proficiency in classic shell scripting in a Linux environment (POSIX tools, Bash, awk)
Salary: Commensurate with qualifications and experience (between 45-55K?). Other benefits: complementary health insurance and meal vouchers
Applicants should email a cover letter addressing the points listed above together with a curriculum vitae to: job@elda.org
ELDA is acting as the distribution agency of the European Language Resources Association (ELRA). ELRA was established in February 1995, with the support of the European Commission, to promote the development and exploitation of Language Resources (LRs). Language Resources include all data necessary for language engineering, such as monolingual and multilingual lexica, text corpora, speech databases and terminology. The role of this non-profit membership Association is to promote the production of LRs, to collect and to validate them and, foremost, make them available to users. The association also gathers information on market needs and trends.
For further information about ELDA/ELRA, visit: ww.elra.info
(2019-07-19) Two Post-doctoral positions at Le Mans University , France
2 Post-doctoral positions at Le Mans University on Deep learning approaches speech processing
*Place of work* Le Mans University, Le Mans ? France
*Starting date* From now to June 2020
*Salary* between 2 300 and 2 600 ? /month
*Duration* 12 months and 24 months (can be combined in a 36 months position)
**************************************** 1st position ****************************************
* Context * The LST team from LIUM (Le Mans University) is focusing on autonomous system?s behavior for the task of speaker diarization and machine translation. The ALLIES project (European Chist-ERA collaborative project) aims at developing evaluation protocols, metrics and scenarios for lifelong learning autonomous systems. The goal is to enable auto-adaptable systems that can also auto-evaluate in order to sustain their performance across time. Autonomous systems can rely on human domain experts via active and interactive learning processes to be define within the ALLIES project.
* Missions * Develop an autonomous system for speaker diarization by integrating lifelong learning, active and interactive learning components. The research work will be related to some of the following topics: - unsupervised adaptation - unsupervised evaluation - active learning (based on the unsupervised evaluation process, the autonomous system is free to require additional knowledge from the human domain expert) - Interactive learning (a human domain expert provides specific knowledge to the autonomous system. This information must be taken into account by the system) Performance will be analyzed using protocols, metrics and scenarios developed for the ALLIES project.
Participation to the ALLIES benchmarking evaluation for speaker diarization. During the ALLIES project, LIUM is organizing two international evaluation campaigns (one for Speaker Diarization jointly organized with Albayzin and the second one for Machine Translation jointly with WMT) The benchmarking evaluation will serve to validate approaches developed during the post-doc
* Dissemination* The research will be published in the major conferences and journals
Expected competences: - Phd in Machine Learning and Deep Learning - Experience in speech processing is positive - Python fluent - familiar with a deep learning toolkit (Pytorch, TensorFlow)
**************************************** 2nd position ****************************************
* Context * The LST team from LIUM (Le Mans University) is focusing on evolutive end-to-end neural networks for speaker recognition. The Extensor project (French ANR funded) aims at developing novel architectures for end-to-end speaker recognition as well as explaining the behavior of those networks. The focus of Extensor is threefold: get rid of the legacy of bayesian system?s architecture and explore wider opportunities offered in deep learning; explore real end-to-end architectures exploiting the tax signal instead of classical features (such as MFCC of filterbanks); Develop tools for explainability in speaker recognition.
* Missions * Develop end-to-end speaker recognition system based on state-of-the-art approaches (x-vectors, sincnet?) Develop evolutive architectures making use of existing genetic algorithms and study their behavior. Participate to the three hackathons organized by the Extensor project in order to develop tools for evolutive neural network architecture and explainability for speaker recognition. Dissemination: the research will be published in the major conferences and journals
Expected competences: - Phd in Machine Learning and Deep Learning - Experience in speech processing is positive - Python fluent - familiar with a deep learning toolkit (Pytorch, TensorFlow)
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Anthony Larcher Maître de Conférences, HDR / Associate Professor Directeur de l'Institut Informatique Claude Chappe co-responsable de la Spécialité Informatique Responsable de l'option Interface Personnes Systèmes Tél. +33 (0)2 43 83 38 30 Avenue Olivier Messiaen, 72085 - LE MANS Cedex 09 univ-lemans.fr
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma
Keywords: hate speech, social media, natural language processing.
The rapid development of the Internet and social networks has brought great benefits to women and men in their daily lives. Unfortunately, the dark side of these benefits has led to an increase in hate speech and terrorism as the most common and powerful threats on a global scale. Hate speech is a type of offensive communication mechanism that expresses an ideology of hatred often using stereotypes. Hate speech can target different societal characteristics such as gender, religion, race, disability, etc. Hate speech is the subject of different national and international legal frameworks. Hate speech is a type of terrorism and often follows a terrorist incident or event.
Social networks are incredibly popular today. Nowadays, Twitter, LinkedIn, Facebook and YouTube are used as a standard tool for communicating ideas, beliefs and feelings. Only a small percentage of people use part of the network for unhealthy activities such as hate speech and terrorism. But the impact of this low percentage of users is extremely damaging. For years, social media companies such as Twitter, Facebook and YouTube have invested hundreds of millions of dollars each year in the task of detecting, classifying and moderating hate. But these efforts are mainly based on manually revising the content to identify and remove offensive content, which is extremely expensive.
This thesis aims at designing automatic and evolving methods for the classification of hate speech in the field of social media. Despite the studies already published on this subject, the results show that the task remains very difficult. We will use semantic content analysis methodologies from automatic language processing (NLP) and methodologies based on deep learning (DNN) which is the revolution in the field of artificial intelligence. During this thesis, we will develop a research protocol to classify hate speech in the text in terms of hateful, aggressive, insulting, ironic, neutral, etc. character. This type of problem is placed in the context of the multi-label classification.
In addition, the problem of obfuscation of words in hate messages will need to be addressed. People who want to write hate speech on the Internet know that they risk being censored by rudimentary automatic systems of moderation. So, users try to obscure their words by changing the spelling or the spelling of words.
Among the crucial points of this thesis are the choice of the DNN architecture and the relevant representation of the data, ie the text of the internet message. The system designed will be validated on real flows of social networks.
Skills
Strong background in mathematics, machine learning (DNN), statistics
Following profiles are welcome, either: Strong experience with natural language processing
Excellent English writing and speaking skills are required in any case.
References :
T Gröndahl, L Pajola, M Juuti, M Conti, N Asokan (2018) ?All You Need is? Love?: Evading Hate-speech Detection, arXiv preprint arXiv:1808.09115
Wiegand, M., Klakow, D. (2008). Optimizing Language Models for Polarity Classification. In Proceedings of ECIR, pp. 612-616.
Wiegand, M., Ruppenhofer, J. (2015). Opinion Holder and Target Extraction based on the Induction of Verbal Categories. In Proceedings of CoNLL, pp. 215-225.
Wiegand, M., Ruppenhofer J., Schmidt A., C. Greenberg (2018) Inducing a Lexicon of Abusive Words ? A Feature-Based Approach. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
Wiegand, M., Wolf, M., Ruppenhofer, J. (2017) Negation Modeling for German Polarity Classification. In Proceedings of GSCL.
Zhang Z., Luo L. (2018). Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter. arxiv.org/pdf/1803.03662
In the context of seed funding for AI research in Flanders prof. Bart de Boer is looking for a PhD student for the origins of language group of the AI-lab of the Vrije Universiteit Brussels.
PhD position offered
We offer a four year PhD position funded by a scholarship with a yearly bench fee. The PhD work will consist of building an agent-based simulation in which we can investigate emergence of behavior in a cognitively realistic setting. This means that the agents are not fully rational and that they show behavior similar to that of humans, and that interests of agents are not necessarily always aligned. The modeling will primarily focus on emergence of speech, but the simulation should be general enough that it can be easily adapted to other areas, such as traffic or economic interactions.
What we are looking for
We are looking for an enthusiastic student with a degree in artificial intelligence, cognitive science, linguistics or equivalent and who has experience programming agent-based or cognitive models, preferably in Python or C++. Knowledge of speech and speech processing is a bonus. The starting date is negotiable, but preferably no later than September 2019.
How to apply
Send a recent CV, detailing your academic record and your programming experience as well as a letter of motivation to prof. Bart de Boer. At this stage we ask you not to send copies of your diplomas or letters of reference. These we will request directly if we decide to further pursue your application, If you have any questions please email prof. Bart de Boer.
(2019-07-29) Visiting postdoc at Vrije Universiteit, Brussels, Belgium
Visiting postdoc in
Cognitively Plausible Emergent Behavior
In the context of seed funding for AI research in Flanders prof. Bart de Boer is looking for a short term (three-six months) visiting postdoc for the origins of language group of the AI-lab of the Vrije Universiteit Brussels.
Position offered
We offer a three-six months visiting postdoc position funded by a scholarship and with a bench fee. The work should consist of agent-based simulation, or of experiments to investigate emergence of behavior in a cognitively realistic setting. This means that in a computer simulation, the agents are not fully rational and that they show behavior similar to that of humans, and that interests of agents are not necessarily always aligned. Experiments should focus on factors that are typical for human settings, but that are generally idealized away, such as altruism, conflicts of interests and other 'non-rational' behaviors. We are most interested in modeling emergence of speech, but we welcome applications proposing other areas, such as traffic or economic interactions.
What we are looking for
We are looking for an enthusiastic postdoc with a track record in artificial intelligence, cognitive science, linguistics or equivalent and who has either experience programming agent-based or cognitive models, or who has experience with the interaction between computer models and experiments. The starting date is negotiable, but preferably no later than September 2019.
How to apply
Send a recent CV, detailing your academic record and your programming experience as well as a letter of motivation to prof. Bart de Boer. Be sure to include a short (1-page) outline of your proposed project in the letter of motivation, as well as a short planning. At this stage we ask you not to send copies of your diplomas or letters of reference. These we will request directly if we decide to further pursue your application, If you have any questions please email prof. Bart de Boer.
(2019-08-02) Research engineer or Post-doc, at Eurecom, Inria, LIA, France
EURECOM (Nice, France), Inria (Nancy, France) and LIA (Avignon, France) are opening a 18-month Research Engineer or Postdoc position on speaker de-identification and voice privacy.
(2019-08-02) Ph.D. position in Softbank robotics and Telecom-Paris, France
Ph.D. position in Softbank robotics and Telecom-Paris Subject: Automatic multimodal recognition of users? social behaviors in human-robot interactions (HRI)
*Places of work* Softbank Robotics [SB] (Paris 15e) & Telecom Paris [TP] Palaiseau (Paris outskirt)
*Context* The research activity of the Ph.D. candidate will contribute to : - Softbank Robotics robot?s software NAOqi, within the Expressivity team responsible for ensuring an expressive, natural and fun interaction with our robots. - the Social Computing topic [SocComp.] of the S2a team [SSA] at Telecom-ParisTech, in close collaboration with other researchers and Ph.D. students of the team.
* Candidate profile* As a minimum requirement, the successful candidate should have: ? A master in one or more of the following areas: human-agent interaction, deep learning, computational linguistics, cognitive sciences, affective computing, reinforcement learning, natural language processing, speech processing ? Excellent programming skills (preferably in Python) ? Excellent command of English ? Very good communication skills, commitment, independent working style as well as initiative and team spirit
Given the multidisciplinary aspect of the subject, priority will be given to multidisciplinary profiles. Ph.D. applicant?s interest in social robotics is required.
*Keywords* Human-Machine Interaction, Social Robotics, Deep Learning, Social Computing, Natural Language Processing, Speech Processing, Computer Vision, Multimodality
*How to apply* Applications should be sent as soon as possible (the first review of applications will be made in early September). The application should be formatted as **a single pdf file** and should include: ? A complete and detailed curriculum vitae ? A letter of motivation ? The academic credentials and the transcript of grades ? The contact of two referees
*Description* Social robotics, and more broadly human-agent interaction is a field of human-machine interaction for which the integration of social behaviors is expected to have great potential. 'Socio-emotional behaviors' (emotions, social stances) include thus the role and the reactions of the user towards the robot during an interaction. These behaviors could be expressed differently depending: -on the user (age, emotional state, ...): some users may have a dominant behavior with the robot, considering it a tool to achieve a goal. Others are more cooperative with the robot, they can be more friendly with it. Still others try to trap or 'troll' the robot. -on the interaction context (users do not behave in the same way when interacting with a pepper selling toys, or with a pepper bank secretary). Besides, in each of these situations, the robot must be able to adapt its behavior, and to provide a coherent interaction between the user and the robot, avoiding confusion and frustration.
This Ph.D. will focus on multimodal modeling for the prediction of the user's socio-emotional behaviors during interactions with a robot and on building an engine that is robust to real-life scenarios and different contexts. In particular, the Ph.D. candidate will address the following points: - the encoding of contextual multimodal representations relevant for the modeling of socio-emotional behavior. Thanks to the robot, we have access to a lot of information on context (market, robot intention, demographics, multi or mono user interaction, etc.) that could be combined to our multimodal representation. - the development and evaluation of models that take advantage of the complementarity of modalities in order to monitor the evolution of the user's socio-emotional behaviors during the interaction (e. g. taking into account the inherent sequentially of the interaction structure) The models will be based on sequential neural approaches (recurrent networks) that integrate attention models as a continuation of the work done in [Hemamou] and [BenYoussef19].
Selected references of the team: [Hemamou] L. Hemamou, G. Felhi, V. Vandenbussche, J.-C. Martin, C. Clavel, HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews. in AAAI 2019 [Garcia] Alexandre Garcia, Chloé Clavel, Slim Essid, Florence d?Alche-Buc, Structured Output Learning with Abstention: Application to Accurate Opinion Prediction, ICML 2018 [Clavel&Callejas] Clavel, C.; Callejas, Z., Sentiment analysis: from opinion mining to human-agent interaction, Affective Computing, IEEE Transactions on, 7.1 (2016) 74-93. [Langlet] C. Langlet and C. Clavel, Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user?s likes and dislikes , in ACL 2015 [Maslowski] Irina Maslowski, Delphine Lagarde, and Chloé Clavel. In-the-wild chatbot corpus: from opinion analysis to interaction problem detection, ICNLSSP 2017. [Ben-Youssef17] Atef Ben-Youssef, Chloé Clavel, Slim Essid, Miriam Bilac, Marine Chamoux, and Angelica Lim. Ue-hri: a new dataset for the study of user engagement in spontaneous human-robot interactions. In Proceedings of the 19th ACM International Conference on Multimodal Interaction, pages 464?472. ACM, 2017. [Ben-Youssef19] Atef Ben Youssef; Chloe Clavel; Slim Essid Early Detection of User Engagement Breakdown in Spontaneous Human-Humanoid Interaction, IEEE Transactions on Affective Computing, 2019 [Varni] Varni G., Hupont, I., Clavel, C., Chetouani, M. Computational Study of Primitive Emotional Contagion in Dyadic Interactions. IEEE Transactions on Affective Computing, 2017.
(2019-08-12) Several positions in Forensic Speech Science or Forensic Data Science: Aston University, Birmingham, UK
Positions in Forensic Speech Science or Forensic Data Science:
- One Lecturer or Senior Lecturer
- Two Postdoctoral Researchers
Aston University, Birmingham, UK
Aston University has recently been awarded GBP 5.4 M from Research England?s Expanding Excellence in England (E3) Fund. The money is being used to expand the existing Centre for Forensic Linguistics into the substantially larger Aston Institute for Forensic Linguistics (AIFL). As part of the expansion, we are building a research team with expertise in forensic speech science and in forensic data science. In addition to conducting research in forensic speech science, members of the team will work on forensic inference and statistics more broadly, and on quantitative-measurement and statistical-model based approaches in other branches of forensic science. The latter potentially include but are not limited to: fingerprints, face, gait, ballistics, blood pattern analysis, and linguistics. The Forensic Speech Science Laboratory and the Centre for Forensic Data Science will be headed by Dr Geoffrey Stewart Morrison, and, in addition to the affiliation with AIFL, will be affiliated with the Computer Science Department in the School of Engineering and Applied Science.
We are looking to recruit the following positions:
Lecturer or Senior Lecturer in Forensic Speech Science or Forensic Data Science
Closing Date: 23.59 hours BST on September 30, 2019
Interview Date: To be confirmed
The Lecturer or Senior Lecturer position will be a full-time permanent position and will include teaching and administrative responsibilities. The position is costed as a Grade 9 Lecturer, but an exceptionally well qualified and experienced successful applicant could potentially be appointed as a Grade 10 Senior Lecturer. Note: ?Lecturer? is equivalent to North American ?Assistant Professor?, ?Senior Lecturer? is equivalent to North American ?Associate Professor?, and ?Reader / Associate Professor? is an occasionally used additional rank between Senior Lecturer and Professor.
The Postdoctoral Researcher positions may be filled as full-time appointments (preferred) or via a combination of part-time appointments. The Postdoctoral Researcher positions will be fixed-term, but the plan is to build a team that will be successful in obtaining additional research funding that will sustain these positions.
All new team members must have a commitment to solving forensic problems. Previous experience working on forensic problems would be advantageous, but not essential. A background in forensic speech science, in other branches of forensic science, and/or in forensic inference and statistics would be advantageous, but not essential. At least one of the new team members must have a strong background in state-of-the-art automatic speaker recognition, with an ability to implement systems. Other useful backgrounds for members of the team would include biometrics, machine learning, natural language processing, and acoustic phonetics.
Candidates may apply for both the Lecturer / Senior Lecturer and the Research Associate positions. If positions are not filled after this round of recruitment, we will initiate another round of recruitment.
We also welcome enquiries from individuals who have obtained or are applying for their own postdoctoral fellowships, e.g., Marie Sklodowska-Curie Fellowships. For suitable candidates we would assist with the application process.
Potential candidates are encouraged to contact Dr Geoffrey Stewart Morrison to seek more information about these positions.
Tel: +44 121 204 3901
e-mail: g.s.morrison@aston.ac.uk
Dr Morrison will be attending Interspeech in September and would be happy to meet informally with potential applicants there.
We are looking for a postdoc to conduct research in a multidisciplinary expedition project funded by Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden?s largest individual research program, addressing compelling research topics that promise disruptive innovations in AI, autonomous systems and software for several years to come.
The project combines Formal Methods and Human-Robot Interaction with the goal of moving from conventional correct-by-design control with simple, static human models towards the synthesis of correct-by-design and socially acceptable controllers that consider complex human models based on empirical data. Two demonstrators, an autonomous driving scenario and a mobile robot navigation scenario in crowded social spaces, are planned to showcase the advances made in the project.
The focus of this position is on the development of data-driven models of human behavior that can be integrated with formal methods-based systems to better reflect real-world situations, as well as in the evaluation of the social acceptability of such systems.
The candidate will work under the supervision of Assistant Prof. Iolanda Leite (https://iolandaleite.com/) and in close collaboration with another postdoctoral researcher working in the field of formal synthesis.
This is a two-year position. The starting date is open for discussion, but ideally, we would like the selected candidate to start ASAP.
QUALIFICATIONS
Candidates should have completed, or be near completion of, a Doctoral degree with a strong international publication record in areas such as (but not limited to) human-robot interaction, social robotics, multimodal perception, and artificial intelligence. Familiarity with formal methods, game theory, and control theory is an advantage.
Documented written and spoken English and programming skills are required. Experience with experimental design and statistical analysis is an important asset. Applicants must be strongly motivated, be able to work independently and possess good levels of cooperative and communicative abilities.
We look for candidates who are excited about being a part of a multidisciplinary team.
HOW TO APPLY
The application should include:
1. Curriculum vitae.
2. Transcripts from University/ University College.
3. A brief description of the candidate's research interests, including previous research and future goals (max 2 pages).
4. Contact of two references. We will contact the references only for selected candidates.
The application documents should be uploaded using the KTH's recruitment system:
The application deadline is ** September 13, 2019 **
----------------- Iolanda Leite Assistant Professor KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Robotics, Perception and Learning (RPL)
Teknikringen 33, 4th floor, room 3424, SE-100 44 Stockholm, Sweden Phone: +46-8 790 67 34 https://iolandaleite.com
(2019-08-17) Fully funded PhD position at IDIAP, Martigny, Valais, Switzerland.
There is a fully funded PhD position open at Idiap Research Institute on spiking neural architectures for speech prosody.
The research will build on work done recently at Idiap on creating tools for physiologically plausible modelling of speech. The current 'toolbox' contains rudimentary muscle models and means to drive these using conventional (deep) neural networks. The main focus of the work will involve use of spiking neural networks such as the 'integrate and fire' type that is broadly representative of those found in biological systems. Whilst we have focused so far on prosody (actually intonation), the application is open ended; the focus is on the neural modelling. A key problem to be solved will be that of training of the spiking networks, especially with the recurrence that is usual in such networks. We hope to be able to train and use spiking networks as easily as conventional backpropagation networks, and to shed light on current understanding of how biological spiking networks learn (e.g., via spike timing-dependent plasticity).
Idiap is located in Martigny in French speaking Switzerland, but functions in English and hosts many nationalities. PhD students are registered at EPFL. All positions offer quite generous salaries. Martigny has a distillery and a micro-brewery and is close to all manner of skiing, hiking and mountain life.
(2019-08-18) PhD positions at IRIT, Toulouse, France
Applications are invited for a three-year Early Stage Researcher PhD positions in the speech technology for pathological speech.
Description
The thesis focuses on studying the link between the internal representations of Deep Neural Networks (DNNs) and the subjective representation of speech intelligibility. We propose to explore the saliency detection capabilities of DNNs when used in a regression task for predicting speech intelligibility scores as given by human experts. By saliency, we mean to retrieve which frequency bands are important and used by a DNN to make its predictions.
The final expectation is to identify regions of interest in the speech signal, both in time and frequency, that characterise the level of speech impairment.
The experiments will be processed on various samples of speech performed by 150 people (100 patients and 50 healthy controls). This database was recorded within the INCA C2SI project, and contains speech from patients treated for cancer of the oral cavity or pharynx. It contains also various metadata such as the location of the tumor, the impairment in terms of severity and intelligibility that were appreciated by human experts, self evaluation questionnaires on the patient?s quality of life? Various tasks were recorded such as a sustained vowel, read speech, nonsense words, prosodic exercises, picture description, etc.
There will be also the possibility to extend the work to another corpus which is composed of voice of patients suffering from Parkinson disease.
At first, the PhD will have to take benefit from the various analysis and descriptions that were done during the C2SI project trying to correlate the impact of the tumor and the communication ability. Those results will help attesting the human representation of the impact of the disease. Then, a DNN representation will be modeled to fit the data, taking care of the data sparsity. The last part of the work will be to explore the intern representation of the DNN, trying to explore what part of the signal help to make a decision on the impact of the disease and that will be the final goal of the thesis, studying the automatic representation that lies in the model the student will propose.
This work is funded by the TAPAS project (https://www.tapas-etn-eu.org) which is a Horizon 2020 Marie Sk?odowska-Curie Actions Initial Training Network European Training Network (MSCA-ITN-ETN) project that aims to transform the well being of people across Europe with debilitating speech pathologies (e.g., due to stroke, Parkinson's, etc.). These groups face communication problems that can lead to social exclusion. They are now being further marginalised by a new wave of speech technology that is increasingly woven into everyday life but which is not robust to atypical speech.
The supervision of the PhD will take place at IRIT laboratory by the SAMoVA team in Toulouse. SAMoVA does research in the domain of ?analysis, modeling and structuring of audiovisual content?. The application areas are diverse: speech processing, identification of languages, speaker verification and speech and music indexing. The researchers expertise covers novel machine learning and audio processing technologies and is now focused on deep learning methods, leading to several publications in international conferences.
Eligibility Criteria:
Early Stage Researchers (ESRs) shall, at the time of recruitment by the host organization, be in the first four years (full-time equivalent research experience) of their research careers.
- The ESR may be a national of a Member State, of an Associated Country or of any Third Country. - The ESR must not have resided or carried out her/his main activity (work, studies, etc.) in the country of her/his host organization for more than 12 months in the 3 years immediately prior to her/his recruitment. - Holds a Master?s degree or equivalent, which formally entitles to embark on a Doctorate. - Does not hold a PhD degree.
Keywords: discriminative patternmining, neural networks analysis, explainability of black box models, speech recognition.
Deadline to apply: September 30th, 2019
Context:
Understanding the inner working of deep neural networks (DNN) has attracted a lot of attention in the past years [1, 2] and most problems were detected and analyzed using visualization techniques [3, 4]. Those techniques help to understand what an individual neuron or a layer of neurons are computing. We would like to go beyond this by focusing on groups of neurons which are commonly highly activated when a network is making wrong predictions on a set of examples. In the same line as [1], where the authors theoretically link how a training example affects the predictions for a test example using the so called ?influence functions?, we would like to design a tool to ?debug? neural networks by identifying, using symbolic data mining methods, (connected) parts of the neural network architecture associated with erroneous or uncertain outputs.
In the context of speech recognition, this is especially important. A speech recognition system contains two main parts: an acoustic model and a language model. Nowadays models are trained with deep neural networks-based algorithms (DNN) and use very large learning corpora to train an important number of DNN hyperparameters. There are many works to automatically tune these hyperparameters. However, this induces a huge computational cost, and does not empower the human designers. It would be much more efficient to provide human designers with understandable clues about the reasons for the bad performance of the system, in order to benefit from their creativity to quickly reach more promising regions of the hyperparameter search space.
Description of the position:
This position is funded in the context of the HyAIAI ?Hybrid Approaches for Interpretable AI? INRIA project lab (https://www.inria.fr/en/research/researchteams/inria-project-labs). With this position, we would like to go beyond the current common visualization techniques that help to understand what an individual neuron or a layer of neurons is computing, by focusing on groups of neurons that are commonly highly activated when a network is making wrong predictions on a set of examples. Tools such as activation maximization [8] can be used to identify such neurons. We propose to use discriminativepatternmining, and, to begin with, the DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the discriminative activation patterns among the identified neurons.
The data will be provided by the MULTISPEECH team and will consist of two deep architectures as representatives of acoustic and language models [9, 10]. Furthermore, the training data will be provided, where the model parameters ultimately derive from. We will also extend our results by performing experiments with supervised and unsupervised learning to compare the features learned by these networks and to perform qualitative comparisons of the solutions learned by various deep architectures. Identifying ?faulty? groups of neurons could lead to the decomposition of the DL network into ?blocks? encompassing several layers. ?Faulty? blocks may be the first to be modified in the search for a better design.
The recruited person will benefit from the expertise of the LACODAM team in patternmining and deep learning (https://team.inria.fr/lacodam/) and of the expertise of the MULTISPEECH team (https://team.inria.fr/multispeech/) in speech analysis, language processing and deep learning. We would ideally like to recruit a 1 year (with possibly one additional year) post-doc with the following preferred skills: ? Some knowledge (interest) about speech recognition ? Knowledgeable in patternmining (discriminative patternmining is a plus) ? Knowledgeable in machine learning in general and deep learning particular ? Good programming skills in Python (for Keras and/or Tensor Flow) ? Very good English (understanding and writing)
See the INRIA web site for the post-doc page.
The candidates should send a CV, 2 names of referees and a cover letter to the four researchers (firstname.lastname@inria.fr) mentioned above. Please indicate if you are applying for the post-doc or the PhD position. The selected candidates will be interviewed in September for an expected start in October-November 2019.
Bibliography:
[1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence Functions. ICML 2017: pp 1885-1894 (best paper).
[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals: Understanding deep learning requires rethinking generalization. ICLR 2017.
[3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp 427-436.
[4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks. ICLR 2014.
[5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi: Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.
[6] Kailash Budhathoki and Jilles Vreeken. The difference and the norm?characterising similarities and differences between databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 206?223. Springer, 2015.
[7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An efficient algorithm for enumerating frequent closed item sets. In Fimi, volume 90. Citeseer, 2003.
(2019-08-28) Speech technologist/linguist at Cobaltspeech.
Cobalt Speech & Language (http://www.cobaltspeech.com/ ) is looking for a speech technologist/linguist to help find and create language resources for a project in French Canadian.
The project is short term (<2 months) part time (~5-8h a week) which is ideal for a student to get an experience with speech industry.
The following skills are required: - Native French (does not have to be Canadian - though desirable) - Able to communicate in English - Basic understanding of speech technology and linguistics - Ability to run a python script.
52019-09-05) Post doctoral position at IDIAP, Martigny, Switzerland
The Social Computing Group at Idiap is seeking a creative and motivated postdoctoral researcher to work on deep learning methods for behavioral analysis from video and audio data. This is an opening for a researcher with experience in deep learning applied to dynamic human behavior (from voice, body, or face), in the context of a project funded by Innosuisse, the Swiss funding agency for promotion of innovation.
The position offers the opportunity to do exciting work on deep learning and social behavior. The researcher will work with Prof. Daniel Gatica-Perez and his research group. The candidates will have a PhD degree in computer science or engineering, with proven experience in deep learning and a strong publication record.
Salaries are competitive and starting date is immediate. Interviews will start upon reception of applications until the positions are filled.
Interested candidates are invited to submit a cover letter, a detailed CV, and the names of three references through Idiap's online recruitment system:
Interested candidates can also contact Prof. Daniel Gatica-Perez (gatica@idiap.ch).
About Idiap Research Institute
Idiap is an independent, not-for-profit, research institute recognized and funded by the Swiss Federal Government, the State of Valais, and the City of Martigny. Idiap is an equal opportunity employer, and offers competitive salaries and excellent working conditions in a dynamic and multicultural environment.
Idiap is located in the town of Martigny in Valais, a scenic region in the south of Switzerland, surrounded by the highest mountains of Europe, and offering exceptional quality of life, exciting recreational activities, including hiking, climbing and skiing, as well as varied cultural activities, all within close proximity to Lausanne and Geneva. English is the official working language.
ZAION est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l’Intelligence Artificielle.
ZAION a développé une solution qui s’appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 18 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP …).
Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c’est prendre part à une aventure passionnante au sein d’une équipe ambitieuse afin de devenir la référence sur le marché des robots conversationnels.
Nous rejoindre, c’est prendre part à une aventure passionnante et innovante afin de devenir la référence sur le marché des robots conversationnels. Dans le cadre de son développement ZAION recrute son Data Scientist /Machine Learning appliqué à l’Audio H/F. Au sein de l’équipe R&D, votre rôle est stratégique dans le développement et l’expansion de la société. Vous développerez, une solution qui permet de détecter les émotions dans les conversations. Nous souhaitons augmenter les fonctionnalités cognitives de nos callbots afin qu’ils puissent détecter les émotions de leurs interlocuteurs (joie, stress, colère, tristesse…) et donc adapter leurs réponses en conséquence.
Vos missions principales :
- Vous participez à la création du pôle R&D de ZAION et piloterez à votre arrivée votre premier projet de reconnaissance d’émotion dans la voix.
- Construisez, adaptez et faites évoluer nos services de détection d’émotion dans la voix
- Analysez de bases de données conséquentes de conversations pour en extraire les conversations émotionnellement pertinentes
- Construisez une base de données de conversations labelisées avec des étiquettes émotionnelles
- Formez et évaluez des modèles d'apprentissage automatique pour la classification d’émotion
- Déployez vos modèles en production
- Améliorez en continue le système de détection des émotions dans la voix
Qualifications requises et expérience antérieure :
-Vous avez une expérience de 2 ans minimum comme Data Scientist/Machine Learning appliqué à l’Audio
- Diplômé d’une école d’Ingénieur ou Master en informatique ou un doctorat en informatique mathématiques avec des compétences solides en traitements de signal (audio de préférence)
- Solide formation théorique en apprentissage machine et dans les domaines mathématiques pertinents (clustering, classification, factorisation matricielle, inférence bayésienne, deep learning...)
- La mise à disposition de modèles d'apprentissage machine dans un environnement de production serait un plus
- Vous maîtrisez un ou plusieurs des langages suivants : Python, Frameworks de machine Learning/Deep Learning (Pytorch, TensorFlow,Sci-kit learn, Keras) et Javascript
- Vous maîtrisez les techniques du traitement du signal audio
- Une expérience confirmée dans la labélisation de grande BDD (audio de préférence) est indispensable ;
- Votre personnalité : Leader, autonome, passionné par votre métier, vous savez animer une équipe en mode projet
- Vous parlez anglais couramment
Merci d’envoyer votre candidature à : alegentil@zaion.ai
(2019-09-15) Post-doc and research engineer at INSA, Rouen, Normandy, France
Post-doctoral position (1 year): Perception for interaction and social navigation
Research Engineer (1 year): Social Human-Robot Interactions
Laboratory: LITIS, INSA Rouen Normandy, France
Project: INCA (Natural Interactions with Artificial Companions)
Summary:
The emergence of interactive robots and connected objects has lead to the appearance of symbiotic systems made up of human users, virtual agents and robots in social interactions. However, two major scientific difficulties are unsolved yet: on the one hand, the recognition of human activity remains inaccurate, both at the operational level (location, mapping and identification of objects and users) and cognitive (recognition and tracking of users? intentions) and, on the other hand, interaction involves different modalities that must be adapted according to the context, the user and the situation. The INCA project aims at developing artificial companions (interactive robots and virtual agents) with a particular focus on social interactions. Our goal is to develop new models and algorithms for intelligent companions capable of (1) perceiving and representing an environment (real, virtual or mixed) consisting of objects, robots and users; (2) interacting with users in a natural way to assess their needs, preferences, and engagement; (3) learning models of user behavior and (4) generating semantically adequate and socially appropriate responses.
Post-doctoral position in perception for interaction and social navigation (1 position)
The candidate will work to ensure that a robot can recognize the physical content of the scene surrounding him, recognize himself, static and dynamic objects (users and other robots) and finally predict the movement of dynamic elements. The integration of data from different sensors should allow the mapping of an unknown environment and estimate the position of the robot. First, VSLAM techniques (Visual Simultaneous Localization And Mapping) (Saputra 2018) will be used to map the scene. The regions (or points) of interest detected could then be used to detect obstacles. In order to distinguish between static and dynamic objects, methods of separating the background from the foregound of the scene (Kajo et al, 2018) will be used. Finally, some recent techniques of the Flownet 2.0 type (Eddy et al, 2017), for the prediction of the motion on a video sequence should make it possible to predict the next movement of an object dynamic object and the to apprehend its behavior.
Profile: the candidate must have strong skills in mobile robotics and navigation techniques (VSLAM, OrbSlam, Optical Flow, stereovision...) and a high programming capacities under ROS or any other programming language compatible with robotics. Machine learning and Deep learning skills will be highly appreciated.
Research Engineer in Social Human-Robot Interactions (1 position)
The hired research engineer will work closely with the INCA research staff (permanent, PhD and post-doctoral members) and other project partners. This will mainly involve administering the project's Pepper robots, developing the necessary tools, integrating the algorithms developed with the AgentSlang platform (https://agentslang.github.io/) and join the team created to participate in the Robocup 2020 in Bordeaux, @Home league.
Profile: Computer Sciences / Robotics Engineer
Good level in programming (ROS, Python, possibly Java)
Strong knowledge in robotics
Experiences in some of the following areas would be a plus (non-exhaustive list): machine learning, human-machine social interactions, scene perception, spatio-temporal and semantic representation, natural language dialogue.
Duration and remuneration: 1 year, 2480euros/month (gross salary)
Understanding the inner working of deep neural networks (DNN) has attracted a lot of attention in the past years [1, 2] and most problems were detected and analyzed using visualization techniques [3, 4]. Those techniques help to understand what an individual neuron or a layer of neurons are computing. We would like to go beyond this by focusing on groups of neurons which are commonly highly activated when a network is making wrong predictions on a set of examples. In the same line as [1], where the authors theoretically link how a training example affects the predictions for a test example using the so called ?influence functions?, we would like to design a tool to ?debug? neural networks by identifying, using symbolic data mining methods, (connected) parts of the neural network architecture associated with erroneous or uncertain outputs.
In the context of speech recognition, this is especially important. A speech recognition system contains two main parts: an acoustic model and a language model. Nowadays models are trained with deep neural networks-based algorithms (DNN) and use very large learning corpora to train an important number of DNN hyperparameters. There are many works to automatically tune these hyperparameters. However, this induces a huge computational cost, and does not empower the human designers. It would be much more efficient to provide human designers with understandable clues about the reasons for the bad performance of the system, in order to benefit from their creativity to quickly reach more promising regions of the hyperparameter search space.
Description of the position:
This position is funded in the context of the HyAIAI ?Hybrid Approaches for Interpretable AI? INRIA project lab (https://www.inria.fr/en/research/researchteams/inria-project-labs). With this position, we would like to go beyond the current common visualization techniques that help to understand what an individual neuron or a layer of neurons is computing, by focusing on groups of neurons that are commonly highly activated when a network is making wrong predictions on a set of examples. Tools such as activation maximization [8] can be used to identify such neurons. We propose to use discriminative pattern mining, and, to begin with, the DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the discriminative activation patterns among the identified neurons.
The data will be provided by the MULTISPEECH team and will consist of two deep architectures as representatives of acoustic and language models [9, 10]. Furthermore, the training data will be provided, where the model parameters ultimately derive from. We will also extend our results by performing experiments with supervised and unsupervised learning to compare the features learned by these networks and to perform qualitative comparisons of the solutions learned by various deep architectures. Identifying ?faulty? groups of neurons could lead to the decomposition of the DL network into ?blocks? encompassing several layers. ?Faulty? blocks may be the first to be modified in the search for a better design.
The recruited person will benefit from the expertise of the LACODAM team in pattern mining and deep learning (https://team.inria.fr/lacodam/) and of the expertise of the MULTISPEECH team (https://team.inria.fr/multispeech/) in speech analysis, language processing and deep learning. We would ideally like to recruit a 1 year (with possibly one additional year)post-doc with the following preferred skills:
? Some knowledge (interest) about speech recognition
? Knowledgeable in pattern mining (discriminative pattern mining is a plus)
? Knowledgeable in machine learning in general and deep learning particular
? Good programming skills in Python (for Keras and/or Tensor Flow)
? Very good English (understanding and writing)
However, good PhD applications will also be considered and, in this case, the position will last 3 years. The position will be funded by INRIA (https://www.inria.fr/en/). See the INRIA web site for the post-doc and PhD wages.
The candidates should send a CV, 2 names of referees and a cover letter to the four researchers (firstname.lastname@inria.fr) mentioned above. Please indicate if you are applying for the post-doc or the PhD position. The selected candidates will be interviewed in June for an expected start in September 2019.
Bibliography:
[1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence Functions. ICML 2017: pp 1885-1894 (best paper).
[2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals: Understanding deep learning requires rethinking generalization. ICLR 2017.
[3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp 427-436.
[4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks. ICLR 2014.
[5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi: Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.
[6] Kailash Budhathoki and Jilles Vreeken. The difference and the norm?characterising similarities and differences between databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 206?223. Springer, 2015
[7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An efficient algorithm for enumerating frequent closed item sets. In Fimi, volume 90. Citeseer, 2003.
[8] Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. Visualizing higher-layer features of a deep network. University of Montreal, 1341(3):1, 2009.
[9] G. Saon, H.-K. J. Kuo, S. Rennie, M. Picheny: The IBM 2015 English conversational telephone speech recognition system?, Proc. Interspeech, pp. 3140-3144, 2015.
[10] W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, A. Stolcke : The Microsoft 2017 Conversational Speech Recognition System, IEEE ICASSP, 2018.
10h15-11h15 Debra Ziegeler, conférencière invitée (U. Sorbonne Nouvelle) : The future of already in Singapore English: a matter of selective convergence
11h15-11h45 Pause-café
11h45-12h30 Diana Lewis (AMU, LPL) : Grammaticalisation de lexème, de construction : deux cas de développement adverbial en anglais
12h30-14h15 Déjeuner
14h15-15h00 James German (AMU, LPL) : Linguistic adaptation as an automatic response to socio-indexical cues
15h00-15h45 Daniel Véronique (AMU, LPL) : L’« agglutination nominale » dans les langues créoles françaises : un exemple de convergence ?
15h45-16h15 Pause-Café
16h15-17h00 Chady Shimeen-Khan (U. Paris Descartes, CEPED) : Convergences et divergences à des fins discursives à travers l’usage des marqueurs discursifs chez les jeunes Mauriciens plurilingues
17h00-17h45 Sibylle Kriegel (AMU, LPL) : Créolisation et convergence : l’expression du corps comme marque du réfléchi
17h45-18h15 Charles Zaremba : Le CLAIX Cercle Linguistique d’Aix-en-Provence, retrospective
Samedi 19 octobre
10h-10h45 Akissi Béatrice Boutin (ILA-UFHB, Abidjan-Cocody) : Réanalyses avec et sans convergence dans le plurilinguisme ivoirien
10h45-11h30 Massinissa Garaoun (AMU) : Convergence linguistique et cycles : le cas de la négation en arabe maghrébin et en berbère
11h30-12h Pause-café
12h-12h45Nicolas Tournadre (AMU, LACITO) : Phénomènes de copie et de convergence dans les langues du Tibet et de l’Himalaya
12h45-13h30Cyril Aslanov (AMU, LPL) : Convergence and secondary entropy in a macrodiachronic perspective
Le candidat retenu intégrera une équipe en forte croissance spécialisée en évaluation des systèmes d’IA, ainsi qu’un projet européen ambitieux portant sur les systèmes de traitement de la langue évolutifs (en traduction et en diarisation). La caractérisation des performances des systèmes intelligents capables de s’auto-améliorer au fur et à mesure de leur utilisation, par eux-mêmes et par interaction avec l’humain utilisateur, représente un véritable défi que ce post-doctorat propose de relever.
(2019-10-10) Ingenieur de recherche,Lab.national de metrologie et d'essais, Trappes, France
Ingénieur de recherche en
Traitement Automatique du Langage – F/H
Poste en CDI
Localisation : Laboeratoire national de metrologie et d'essais,Trappes
Traitement Automatique du Langage – F/H
Référence : ML/ITAL/DEC
Leader dans l’univers de la mesure et des références, jouissant d’une forte notoriété en France et à l’international, le LNE soutient l’innovation industrielle et se positionne comme un acteur important pour une économie plus compétitive et une société plus sûre.
Au carrefour de la science et de l’industrie depuis sa création en 1901, le LNE offre son expertise à l’ensemble des acteurs économiques impliqués dans la qualité et la sécurité des produits.
Pilote de la métrologie française, notre recherche est au coeur de cette mission de service public et l’une des clés du succès des entreprises.
Nous nous attachons à répondre au besoin industriel et académique de mesures toujours plus justes, dans des conditions de plus en plus extrêmes ou sur les concepts les plus émergents tels que les véhicules autonomes, les nanotechnologies ou la fabrication additive.
Missions :
Vous intégrerez une équipe de six ingénieurs-docteurs régulièrement accompagnés de post-doctorants, doctorants et stagiaires, spécialisée dans l’évaluation et la qualification des systèmes d’intelligence artificielle. Cette équipe est historiquement reconnue pour son expertise dans l’évaluation des systèmes de traitement automatique du langage naturel et le poste proposé doit contribuer à renforcer cette expertise dans un contexte de forte dynamique de croissance.
Depuis quelques années, l’équipe s’est diversifiée en termes de domaines d’application de son expertise d’évaluation des intelligences en traitant de sujets tels que les dispositifs médicaux, les robots industriels collaboratifs, les véhicules autonomes, etc. L’équipe capitalise sur les savoir-faire à la fois divers et ciblés de ses experts (TAL, imagerie, robotique, etc.) afin d’apporter conjointement une solution satisfaisante à la question de l’évaluation et de la certification des systèmes intelligents, condition impérative de leur acceptabilité et faisant l’objet aujourd’hui d’une attention prioritaire des pouvoirs publics.
C’est dans le cadre de la mise en place progressive d’un centre d’évaluation des systèmes intelligents à vocation nationale et internationale qu’elle cherche à accueillir les meilleurs profils de chaque spécialité de l’IA. Les missions principales de ce futur centre sont le développement de nouveaux protocoles d’évaluation, la qualification et la certification de systèmes intelligents, l’organisation de challenges (campagnes de benchmarking), la mise à disposition de ressources expérimentales, le développement et l’organisation du secteur d’activité et la définition de principes, politiques, doctrines et normes à cet effet.
En tant qu’ingénieur-docteur de recherche en TAL, votre champ d’intervention prioritaire sera le traitement de la langue (texte et parole). Vous pourrez également être amené.e à intervenir dans d’autres domaines du traitement de l’information (par exemple sur le traitement de l’image dont la reconnaissance optique de caractères), puis au-delà en fonction des priorités et de vos propres compétences et affinités.
Le poste est évolutif sur le moyen et le long termes en ce qu’il vise à la formation d’experts techniques de stature au moins nationale et ayant vocation à mener eux-mêmes la politique de croissance et de tutelle de leur spécialité, sous réserve du cadre réglementaire et d’orientations générales du LNE ou de ses donneurs d’ordres.
Dans un premier temps, vous couvrirez les missions suivantes :
- Contribution à la R&D et aux actions structurantes (60%) :
Inventaire technique et commercial du besoin et de l’offre, priorisation des marchés et champs techniques à investir
Identification et définition des grandeurs à mesurer, des métriques afférentes, des protocoles d’évaluation et des moyens d’essais nécessaires
Structuration des données de la discipline (TAL) au sein de référentiels et selon des nomenclatures à bâtir
Programmation et conduite d’essais à des fins expérimentales, de recherche itérative et d’étalonnage
Constitution et animation d’un réseau de chercheurs des secteurs public et privé, national et étranger, en appui aux présentes missions
Contribution au montage et à l’exécution de projets de recherche nationaux et européens et de coopérations internationales
Participation aux travaux de planification du LNE : investissements, RH, budgets annuels, perspectives pluriannuelles
Publication et présentation des résultats scientifiques
Encadrement éventuel de doctorants, post-doctorants, stagiaires
- Contribution aux prestations commerciales en TAL (40%) :
Ingénierie linguistique générale (manipulation des données, analyse statistique, etc.)
Prise en charge du besoin client et reformulation dans le cadre d’une offre technique et commerciale
Organisation des tâches pour la réalisation de la prestation, estimation des ressources nécessaires, négociation
Réalisation de ces tâches en coordination avec l’équipe
Production/rédaction des livrables
Présentation des résultats au client
Profil :
Vous êtes titulaire soit d’un doctorat, soit d’un diplôme d’ingénieur avec un minimum de trois ans d’expérience professionnelle, en informatique ou en sciences du langage, avec une spécialisation en traitement automatique de la langue (TAL) et plus généralement en intelligence artificielle. Les expériences professionnelles ou académiques passées en développement et/ou test logiciel, en analyse statistique, ainsi qu’en traitement de la parole ou de l’image seront particulièrement appréciées.
Vous disposez également d’un bon niveau d’anglais et de programmation (C++ et/ou Python), ainsi que d’une expérience en utilisation de Linux.
Dans le cadre de votre prise de poste, vous pourriez être amené.e à suivre des formations complémentaires (par exemple en intelligence artificielle et en cybersécurité).
Vous saurez être à l’initiative, en disposant d’une large autonomie et d’un potentiel de créativité vous permettant d’occuper pleinement votre espace de responsabilité dans un objectif d’excellence. Vous êtes capable de défendre un leadership de par la qualité et la clarté de vos argumentaires.
Déplacements fréquents en région parisienne (une fois par semaine), en province (une à deux fois par mois) et occasionnels dans le monde (une fois par trimestre) dans le cadre de prestations, réunions ou conférences.
(2019-10-13) Postdoctoral Researcher , IRISA, Rennes, France
Postdoctoral Researcher in Multilingual Speech Processing
CONTEXT The Expression research team focuses on expressiveness in human-centered data. In this context, the team has a strong activity in the field of speech processing, especially text-to-speech (TTS). This activity is denoted by regular publications in top international conferences and journals, exposing contributions in topics like machine learning (including deep learning), natural language processing, and speech processing. Team Expression takes part in multiple collaborative projects. Among those, the current position will take part in a large European H2020 project focusing on the social integration of migrants in Europe. Team’s website: https://www-expression.irisa.fr/
PROFILE Main tasks: 1. Design multilingual TTS models (acoustic models, grapheme-to-phoneme, prosody, text normalization...) 2. Take part in porting the team’s TTS system for embedded environments 3. Develop spoken language skill assessment methods
Secondary tasks: 1. Collect speech data 2. Define use cases with the project partners
Environment: The successful candidate will integrate a team of other researchers and engineers working on the same topics.
Required qualification: PhD in computer science or signal processing Skills: • Statistical machine learning and deep learning • Speech processing and/or natural language processing • Strong object-oriented programming skills • Android and/or iOS programming are a strong plus
CONTRACT Duration: 22 months, full time Salary: competitive, depending on the experience. Starting date: 1st, January 2020.
APPLICATION & CONTACTS Send a cover letter, a resume, and references by email to: • Arnaud Delhay, arnaud.delhay@irisa.fr ; • Gwénolé Lecorvé, gwenole.lecorve@irisa.fr ; • Damien Lolive, damien.lolive@irisa.fr . Application deadline: 15th, November 2019. Applications will be processed on a daily basis.
(2019-10-18) FULLY FUNDED FOUR-YEAR PHD STUDENTSHIPS, University Edingurgh, Scotland
FULLY FUNDED FOUR-YEAR PHD STUDENTSHIPS
UKRI CENTRE FOR DOCTORAL TRAINING IN NATURAL LANGUAGE PROCESSING
at the University of Edinburgh?s School of Informatics and School of Philosophy, Psychology and Language Sciences.
Applications are now sought for the CDT?s second cohort of students to start in September 2020
Deadlines: * Non EU/UK : 29th November 2019 * EU/UK : 31st January 2020.
The CDT in NLP offers unique, tailored doctoral training comprising both taught courses and a doctoral dissertation. Both components run concurrently over four years.
Each student will take a set of courses designed to complement their existing expertise and give them an interdisciplinary perspective on NLP. They will receive full funding for four years, plus a generous allowance for travel, equipment and research costs.
The CDT brings together researchers in NLP, speech, linguistics, cognitive science and design informatics from across the University of Edinburgh. Students will be supervised by a team of over 40 world-class faculty and will benefit from cutting edge computing and experimental facilities, including a large GPU cluster and eye-tracking, speech, virtual reality and visualisation labs.
The CDT involves over 20 industrial partners, including Amazon, Facebook, Huawei, Microsoft, Mozilla, Reuters, Toshiba, and the BBC. Close links also exist with the Alan Turing Institute and the Bayes Centre.
A wide range of research topics fall within the remit of the CDT:
Natural language processing and computational linguistics
Speech technology
Dialogue, multimodal interaction, language and vision
Information retrieval and visualization, computational social science
Computational models of human cognition and behaviour, including language and speech processing
Human-Computer interaction, design informatics, assistive and educational technology
Psycholinguistics, language acquisition, language evolution, language variation and change
Linguistic foundations of language and speech processing
The second cohort of CDT students will start in September 2020 and is now open to applications.
Around 12 studentships are available, covering maintenance at the research council rate (https://www.ukri.org/skills/funding-for-research-training , currently £15,009 per year) and tuition fees. Studentships are available for UK, EU and non-EU nationals. Individuals in possession of other funding scholarships or industry funding are also welcome to apply ? please provide details of your funding source on your application.
Applicants should have an undergraduate or master?s degree in computer science, linguistics, cognitive science, AI, or a related discipline. We particularly encourage applications from women, minority groups and members of other groups that are underrepresented in technology.
Application Deadlines In order to ensure full consideration for funding, completed applications (including all supporting documents) need to be received by:
29th November 2019 (non EU/UK) or 31st January 2020 (EU/UK).
CDT in NLP Open Days Find out more about the programme by attending the PG Open Day at the School of Informatics or by joining one of the CDT in NLP Virtual Open Days:
The University of Southern California?s Institute for Creative Technologies (ICT) is an off-campus research facility, located on a creative business campus in the ?Silicon Beach? neighborhood of Playa Vista. We are world leaders in innovative training and education solutions, computer graphics, computer simulations, and immersive experiences for decision-making, cultural awareness, leadership and health. ICT employees are encouraged to develop themselves both professionally and personally, through workshops, invited guest talks, movie nights, social events, various sports teams, a private gym and a personal trainer. The atmosphere at ICT is informal and flexible, while encouraging initiative, personal responsibility and a high work ethic.
We are looking for an accomplished recent PhD graduate to work on a challenging yet exciting NIH-funded 4-year research project. The project seeks to understand the process and success of Motivational Interviewing (MI). Specifically, our project will address shortcomings of current MI coding systems by introducing a novel computational framework that leverages our recent advances in automatic verbal and nonverbal behavior analyses as well as multimodal machine learning. Our framework aims to jointly analyze verbal (i.e., what is being said), nonverbal (i.e., how something is said), and dyadic (i.e., in what interpersonal context something is said) behavior to better identify in-session patient behavior that is predictive of post-session alcohol use. The project is heavily focused on machine learning, NLP, and data mining; it requires no data collection as all data has already been collected.
We are looking to add a talented machine learning (NLP, CV, or signal processing focus) Postdoctoral Research Associate to our interdisciplinary team of machine learning scientists, affective computing experts, and psychiatrists. Join our team's mission to better understand therapy processes and predict outcomes!
Responsibilities include:
? Design and implement state-of-the-art NLP machine learning algorithms to automatically code dyadic MI therapy sessions and predict behavior change in patients.
? Push the envelope on current NLP and multimodal machine learning algorithms to better understand the MI process and outcome.
? Conduct statistical analysis on verbal, nonverbal and dyadic behavioral patterns to describe their relationship with the MI process and outcome.
? Write and lead authorship of high impact conference (ACL, EMNLP, ICMI, CVPR, ICASSP, and Interspeech) and journal papers (PAMI, TAFFC, and TASLP).
? Support and lead graduate, undergraduate students, and summer interns to preprocess and annotate multimodal MI data.
Work collaboratively with:
? Domain experts of MI research to automatically derive meaningful insights for MI research Experts.
? Computer scientists across departments at the highly accomplished and interdisciplinary USC Institute for Creative Technologies
Have fun & learn while working at ICT with a great team and an incredible mission!
Minimum Education: PhD in computer science or engineering with a focus on NLP, CV signal processing or multimodal machine learning.
Minimum Experience: At least 1 year of experience working with data compromising human verbal and/or nonverbal behavior.
Minimum Field of Expertise: Directly related education in research specialization with advanced knowledge of equipment, procedures, and analysis methods.
Skills: Comfortable with machine learning frameworks such as PyTorch or Tensorflow Excellent programming skills in Python or C++ Analysis Assessment/evaluation Communication-written and oral skills Organization Planning Problem identification and resolution Project management Research
(2019-11-03) Ingénieur de recherche, IRIT, Toulouse France
Dans le cadre du laboratoire commun ALAIA, l'IRIT (équipe SAMoVA https://www.irit.fr/SAMOVA/site/) recrute un ingénieur de recherche en CDD pour intégrer son équipe de recherche, travailler dans le domaine de l'IA appliquée à l'apprentissage des langues étrangères et collaborer avec la société Archean Technologie (http://www.archean.tech/archean-labs-en.html).
Poste à pourvoir : Ingénieur de recherche Durée: 12 à 18 mois Prise de poste : possible dès le 1er décembre 2019 Domaine : traitement de la parole, machine learning, analyse automatique de la prononciation Lieu : Institut de Recherche en Informatique de Toulouse (Université Paul Sabatier) - Équipe SAMoVA Profil recherché : titulaire d'un doctorat en informatique, machine learning, traitement de l'audio. Contact : Isabelle Ferrané (isabelle.ferrane@irit.fr) Dossier de candidature : CV, résumé de la thèse, lettre de motivation, recommandations/contacts Détail de l'offre : https://www.irit.fr/SAMOVA/site/assets/files/engineer/ALAIA_ResearchEngineerPosition(1).pdf Salaire : selon expérience
(2019-11-05) Annotateur/Transcripteur H/F at ZAION, Paris, France
ZAION (https://www.zaion.ai) est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l?Intelligence Artificielle.
ZAION a développé une solution qui s?appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 12 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP ?).
Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c?est prendre part à une belle aventure au sein d?une équipe dynamique qui a l?ambition de devenir la référence sur le marché des robots conversationnels.
Au sein de notre activité Intelligence Artificielle, pour appuyer ses innovations constantes concernant l'identification automatique des sentiments et émotions au sein d'interactions conversationnelles téléphoniques, nous recrutons un Annotateur/Transcripteur H/F :
Ses missions principales :
ANNOTER avec exactitude les échanges entre un client et son conseiller selon des balises expliquées sur un guide,
travailler avec minutie à partir de documents audio et texte en français,
se familiariser rapidement avec un logiciel d'annotation dédié,
connaître les outils de travail collaboratif,
utiliser ses connaissances culturelles, langagières et grammaticales pour rendre compte avec une grande précision non seulement de la conversation entre deux interlocuteurs sur un sujet donné mais aussi de la segmentation de leurs propos.
Le profil du candidat :
être locuteur natif et avoir une orthographe irréprochable,
avoir une très bonne maîtrise des environnements Mac OU Windows OU Linux, - faire preuve de rigueur, d?écoute et de discrétion.
Contrat en CDD (temps complet), basé à Paris (75017)
Si intéressé(e), prière de contacter Anne le Gentil/RRH à l?adresse suivante : alegentil@zaion.ai en joignant au mail un C.V
(2019-11-05) Data Scientist /Machine Learning appliqué à l'Audio H/F, at Zaion, Paris, France
ZAION est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l?Intelligence Artificielle.
ZAION a développé une solution qui s?appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 18 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP ?).
Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c?est prendre part à une aventure passionnante au sein d?une équipe ambitieuse afin de devenir la référence sur le marché des robots conversationnels.
Nous rejoindre, c?est prendre part à une aventure passionnante et innovante afin de devenir la référence sur le marché des robots conversationnels. Dans le cadre de son développement ZAION recrute son Data Scientist /Machine Learning appliqué à l?Audio H/F. Au sein de l?équipe R&D, votre rôle est stratégique dans le développement et l?expansion de la société. Vous développerez, une solution qui permet de détecter les émotions dans les conversations. Nous souhaitons augmenter les fonctionnalités cognitives de nos callbots afin qu?ils puissent détecter les émotions de leurs interlocuteurs (joie, stress, colère, tristesse?) et donc adapter leurs réponses en conséquence.
Vos missions principales :
- Vous participez à la création du pôle R&D de ZAION et piloterez à votre arrivée votre premier projet de reconnaissance d?émotion dans la voix.
- Construisez, adaptez et faites évoluer nos services de détection d?émotion dans la voix
- Analysez de bases de données conséquentes de conversations pour en extraire les conversations émotionnellement pertinentes
- Construisez une base de données de conversations labelisées avec des étiquettes émotionnelles
- Formez et évaluez des modèles d'apprentissage automatique pour la classification d?émotion
- Déployez vos modèles en production
- Améliorez en continue le système de détection des émotions dans la voix
Qualifications requises et expérience antérieure :
-Vous avez une expérience de 2 ans minimum comme Data Scientist/Machine Learning appliqué à l?Audio
- Diplômé d?une école d?Ingénieur ou Master en informatique ou un doctorat en informatique mathématiques avec des compétences solides en traitements de signal (audio de préférence)
- Solide formation théorique en apprentissage machine et dans les domaines mathématiques pertinents (clustering, classification, factorisation matricielle, inférence bayésienne, deep learning...)
- La mise à disposition de modèles d'apprentissage machine dans un environnement de production serait un plus
- Vous maîtrisez un ou plusieurs des langages suivants : Python, Frameworks de machine Learning/Deep Learning (Pytorch, TensorFlow,Sci-kit learn, Keras) et Javascript
- Vous maîtrisez les techniques du traitement du signal audio
- Une expérience confirmée dans la labélisation de grande BDD (audio de préférence) est indispensable ;
- Votre personnalité : Leader, autonome, passionné par votre métier, vous savez animer une équipe en mode projet
- Vous parlez anglais couramment
Merci d?envoyer votre candidature à : alegentil@zaion.ai
(2019-11-25) Offre de stage, INRIA Bordeaux, France
Offre de stage M2 (Informatique/traitement du signal)
Deep Learning pour la classification entre la maladie de Parkinson et l'atrophie multisystématisée par analyse du signal vocal
La maladie de Parkinson (MP) et l'atrophie multisystématisée (AMS) sont des maladies neurodégénératives. AMS appartient au groupe des troubles parkinsoniens atypiques. Dans les premiers stades de la maladie, les symptômes de MP et AMS sont très similaires, surtout pour AMS-P où le syndrome parkinsonien prédomine. Le diagnostic différentiel entre AMS-P et MP peut être très difficile dans les stades précoces de la maladie, tandis que la certitude de diagnostic précoce est importante pour le patient en raison du pronostic divergent. Malgré des efforts récents, aucun marqueur objectif valide n'est actuellement disponible pour guider le clinicien dans ce diagnostic différentiel. La besoin de tels marqueurs est donc très élevé dans la communauté de la neurologie, en particulier compte tenu de la gravité du pronostic AMS.
Il est établi que les troubles de la parole, communément appelés dysarthrie, sont un symptôme précoce commun aux deux maladies et d'origine différente. Nous menons ainsi des recherches qui consistent à utiliser la dysarthrie, grâce à un traitement numérique des enregistrements vocaux des patients, comme un vecteur pour distinguer entre MP et AMS-P. Nous coordonnons actuellement un projet de recherche sur cette thématique avec des partenaires cliniciens, neurologues et ORL, des CHU de Bordeaux et Toulouse. Dans le cadre de ce projet nous disposons d?une base de données d?enregistrements vocaux de patients MP et AMS-P (et de sujets saints).
Le but de ce stage est d?explorer des techniques récentes de Deep Leaning pour effectuer la classification entre MP et AMS-P. La première étape du stage consistera en l?implémentation d?un système baseline utilisant des outils standards et en se basant sur la méthodologie décrite dans [1]. Cette dernière traite la classification entre MP et les sujets saints et utilise des «chunks » de spectrogrammes comme entrée à un réseau neuronale convolutionnel (CNN). Cette méthodologie sera appliquée à la tâche MP vs AMS-P en utilisant notre base de données. L?implémentation du CNN se fera avec Keras-Tensorflow (https://www.tensorflow.org/guide/keras). L?extraction des paramètres du signal vocal sera effectuée par Matlab et le logiciel Praat (http://www.fon.hum.uva.nl/praat/). Cette étape permettra au stagiaire d?assimiler les briques de base du Deep Learning et de l?analyse la voix pathologique.
La deuxième étape de stage consistera à développer un réseau de neurones profonds (DNN) qui prend en entrée des représentations acoustiques dédiées à la tâche MP vs AMS-P et développés par notre équipe. Il s?agira de :
construire le bon jeu de données
définir la bonne classe de DNN à utiliser
construire la bonne architecture du DNN
poser la bonne fonction objective à optimiser
analyser et comparer les performances de classification
Cette étape nécessitera une meilleure compréhension des aspects théoriques et algorithmiques du Deep Learning.
Pré-requis : Une bonne connaissance des techniques standards en apprentissage statistique (Machine Learning) et de leur conceptualisation est nécessaire. Un bon niveau en programmation Python est aussi nécessaire. Des connaissances en traitement du signal/image et/ou Deep Learning seraient avantageuses. Un test sera effectué pour vérifier ces pré-requis.
Responsable du stage : Khalid Daoudi (khalid.daoudi@inria.fr)
(2019-11-15) 13 PhD studentships at UKRI Centre for Doctoral Training (CDT), University of Sheffield, UK
UKRI Centre for Doctoral Training (CDT) in Speech and Language Technologies (SLT) and their Applications
Department of Computer Science
Faculty of Engineering
University of Sheffield
Fully-funded 4-year PhD studentships for research in Speech and Language Technologies (SLT) and their Applications
** Apply now for September 2020 intake. Up to 13 studentships available **
Deadline for applications: 31 January 2020.
What makes the SLT CDT different:
Unique Doctor of Philosophy (PhD) with Integrated Postgraduate Diploma (PGDip) in SLT Leadership.
Bespoke cohort-based training programme running over the entire four years providing the necessary skills for academic and industrial leadership in the field, based on elements covering core SLT skills, research software engineering (RSE), ethics, innovation, entrepreneurship, management, and societal responsibility.
The centre is a world-leading hub for training scientists and engineers in SLT ? two core areas within artificial intelligence (AI) which are experiencing unprecedented growth and will continue to do so over the next decade.
Setting that fosters interdisciplinary approaches, innovation and engagement with real world users and awareness of the social and ethical consequences of work in this area.
The benefits:
Four-year fully-funded studentship covering all fees and an enhanced stipend (£17,000 pa)
Generous personal allowance for research-related travel, conference attendance, specialist equipment, etc.
A full-time PhD with integrated PGDip incorporating 6 months of foundational SLT training prior to starting your research project
Supervision from a team of over 20 internationally leading SLT researchers, covering all core areas of modern SLT research, and a broader pool of over 50 academics in cognate disciplines with interests in SLTs and their application
Every PhD project underpinned by a real-world application, directly supported by one of over 30 industry partners. Partners include Google, Amazon, Microsoft, Nuance, NHS Digital and many more
A dedicated CDT workspace within a collaborative and inclusive research environment hosted by the Department of Computer Science
Work and live in Sheffield - a cultural centre on the edge of the Peak District National Park which is in the top 10 most affordable and safest UK university cities.
About you:
We are looking for students from a wide range of backgrounds interested in Speech and Language Technologies.
High-quality (ideally first class) undergraduate or masters (ideally distinction) degree in a relevant discipline. Suitable backgrounds include (but not limited to) computer science, informatics, engineering, linguistics, speech and language processing, mathematics, cognitive science, AI, physics, or a related discipline.
Regardless of background, you must be able to demonstrate mathematical aptitude (minimally to A-Level standard or equivalent) and experience of programming.
We particularly encourage applications from groups that are underrepresented in technology.
Candidates must satisfy the UKRI funding eligibility criteria. Students must have settled status in the UK and have been ?ordinarily resident? in the UK for at least 3 years prior to the start of the studentship. Full details of eligibility criteria can be found on our website.
Applying:
Applications are now sought for the September 2020 intake. Up to 13 studentships available.
We operate a staged admissions process, with application deadlines throughout the year.
The first deadline for applications is 31 January 2020. The second deadline is 31 May 2020.
Applications will be reviewed within 4 weeks of each deadline and short-listed applicants will be invited to interview. Interviews will be held in Sheffield.
In some cases, because of the high volume of applications we receive, we may need more time to assess your application. If this is the case, we will let you know if we intend to do this.
We may be able to consider applications received after 31 May 2020 if places are still available. Equally, all places may be allocated after the first deadline therefore we encourage you to apply early.
See our website for full details and guidance on how to apply: slt-cdt.ac.uk
By replying to this email or contacting sltcdt-enquiries@sheffield.ac.uk you consent to being contacted by the University of Sheffield in relation to the CDT. You are free to withdraw your permission in writing at any time.
(2019-11-21) Bourses en études françaises (MA et PhD) à l'université Western, Canada
Bourses en études françaises (MA et PhD) à l'université Western
Le département d’études françaises de l’université Western (London, Canada) accepte maintenant les demandes d’admission pour l’année académique 2020-2021 pour ses programmes de maîtrise et de doctorat, dans les domaines de la linguistique et de la littérature. L’université Western est reconnue comme une des grandes universités de recherche en Ontario et le département d’études françaises participe activement à maintenir sa réputation depuis plus de 50 ans.
Le corps professoral et l’ensemble des étudiants et étudiantes participant aux programmes d’études supérieures forment une communauté internationale diversifiée. Nous offrons la possibilité de conduire un programme de recherche en linguistique formelle (syntaxe, morphologie, phonologie et sémantique) de même qu’en sociolinguistique.Nous offrons aussi une formation en littérature dans tous les siècles et tous les domaines de la littérature française et francophone, domaines dans lesquels nos étudiants et étudiantes conduisent leur recherche.
Date limite pour le premier appel donnant accès au financement à partir de septembre 2020: 1er février 2020
Les candidatures canadiennes et internationales retenues pour le programme de doctorat reçoivent une bourse d’études d’une durée de quatre ans couvrant les frais de scolarité ainsi qu’un assistanat d’enseignement annuel d’une valeur minimale de $13 000. Le même financement est offert aux étudiants canadiens acceptés à la maîtrise pour une durée d’une année. Les étudiants internationaux acceptés au programme de maîtrise reçoivent un montant forfaitaire de $3 000 pour toute la durée du programme.
En plus des bourses de cycles supérieurs, le département d’études françaises offre aux étudiants et aux étudiantes qui maintiennent un dossier académique de qualité une aide financière pour effectuer des voyages de recherche ou pour prendre part à des colloques, ainsi que la possibilité de remplacer l’assistanat d’enseignement par une bourse de recherche d’une valeur équivalente. Plusieurs étudiants de notre programme de doctorat profitent aussi d’un régime de cotutelle avec une université française.
Pour plus d’information concernant l’aide financière offerte par notre institution, veuillez communiquer directement avec le département d’études françaises ou consultez le lien suivant :http://www.uwo.ca/french/graduate/finances/index.html .
Nous offrons aussi un excellent programme de formation des assistants d’enseignement de même que plusieurs activités de développement professionnel.
Directeur des cycles supérieures : François Poiré (fpoire@uwo.ca)
Adjointe aux cycles supérieurs : Chrisanthi Ballas (frgrpr@uwo.ca)
Required skills: background in statistics, natural language processing and computer program skills (Perl, Python). Candidates should email a detailed CV with diploma
Motivations and context
Recent years have seen a tremendous development of Internet and social networks. Unfortunately, the dark side of this growth is an increase in hate speech. Only a small percentage of people use the Internet for unhealthy activities such as hate speech. However, the impact of this low percentage of users is extremely damaging.
Hate speech is the subject of different national and international legal frameworks. Manual monitoring and moderating the Internet and the social media content to identify and remove hate speech is extremely expensive. This internship aims at designing methods for automatic learning of hate speech detection systems on the Internet and social media data. Despite the studies already published on this subject, the results show that the task remains very difficult (Schmidt et al., 2017; Zhang et al., 2018).
In text classification, text documents are usually represented in some so-called vector space and then assigned to predefined classes through supervised machine learning. Each document is represented as a numerical vector, which is computed from the words of the document. How to numerically represent the terms in an appropriate way is a basic problem in text classification tasks and directly affects the classification accuracy. Developments in Neural Network led to a renewed interest in the field of distributional semantics, more specifically in learning word embeddings (representation of words in a continuous space). Computational efficiency was one big factor which popularized word embeddings. The word embeddings capture syntactic as well as semantic properties of the words (Mikolov et al., 2013). As a result, they outperformed several other word vector representations on different tasks (Baroni et al., 2014).
Our methodology in the hate speech detection is related on the recent approaches for text classification with Neural Networks and word embeddings. In this context, fully connected feed forward networks, Convolutional Neural Networks (CNN) and also Recurrent/Recursive Neural Networks (RNN) have been applied. On the one hand, the approaches based on CNN and RNN capture rich compositional information, and have outperformed the state-of-the-art results in text classification; on the other hand they are computationally intensive and require huge corpus of training data.
To train these DNN hate speech detection systems it is necessary to have a very large corpus of training data. This training data must contains several thousands of social media comments and each comment should be labeled as hate or not hate. It is easy to automatically collect social media and Internet comments. However, it is time consuming and very costly to label huge corpus. Of course, for several hundreds of comments this work can be manually performed by human annotators. But it is not feasible to perform this work for a huge corpus of comments. In this case weakly supervised learning can be used : the idea is to train a deep neural network with a limited amount of labelled data.
The goal of this master internship is to develop a methodology to weakly supervised learning of a hate speech detection system using social network data (Twitter, YouTube, etc.).
Objectives
In our Multispeech team, we developed a baseline system for automatic hate speech detection. This system is based on fastText and BERT embeddings (Bojanowski et al., 2017; Devlin et al, 2018) and the methodology of CNN/RNN. During this internship, the master student will work on this system in following directions:
Study of the state-of-the-art approaches in the field of weakly supervised learning;
Implementation of a baseline method of weakly supervised learning for our system;
Development of a new methodology for weakly supervised learning. Two cases will be studied. In the first case, we train the hate speech detection system using a small labeled corpus. Then, we proceed incrementally. We use this first system to label more data, we retrain the system and use it to label new data, In the second case, we refer to learning with noisy labels (labels that can be not correct or given by several annotators who do not agree).
References
Baroni, M., Dinu, G., and Kruszewski, G. ?Don?t count, predict! a systematic comparison of context-counting vs. contextpredicting semantic vectors?. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Volume 1, pages 238-247, 2014.
Bojanowski, P., Grave, E., Joulin, A., and Mikolov, T. ?Enriching word vectors with subword information?. Transactions of the Association for Computational Linguistics, 5:135?146, 2017.
Dai, A. M. and Le, Q. V. ?Semi-supervised sequence Learning?. In Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems 28, pages 3061-3069. Curran Associates, Inc, 2015.
Devlin J., Chang M.-W., Lee K., Toutanova K. ?BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding?, arXiv:1810.04805v1, 2018.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. ?Distributed representations of words and phrases and their Compositionality?. In Advances in Neural Information Processing Systems, 26, pages 3111-3119. Curran Associates, Inc, 2013b.
Schmidt A., Wiegand M. ?A Survey on Hate Speech Detection using Natural Language Processing?, Workshop on Natural Language Processing for Social Media, 2017.
Zhang, Z., Luo, L. ?Hate speech detection: a solved problem? The Challenging Case of Long Tail on Twitter?. arxiv.org/pdf/1803.03662, 2018.
(2019-11-25) Annotateur/Transcripteur, ZAION, Paris, France
ZAION (https://www.zaion.ai) est une société innovante en pleine expansion spécialisée dans la technologie des robots conversationnels : callbot et chatbot intégrant de l?Intelligence Artificielle.
ZAION a développé une solution qui s?appuie sur une expérience de plus de 20 ans de la Relation Client. Cette solution en rupture technologique reçoit un accueil très favorable au niveau international et nous comptons déjà 12 clients actifs (GENERALI, MNH, APRIL, CROUS, EUROP ASSISTANCE, PRO BTP ?).
Nous sommes actuellement parmi les seuls au monde à proposer une offre de ce type entièrement tournée vers la performance. Nous rejoindre, c?est prendre part à une belle aventure au sein d?une équipe dynamique qui a l?ambition de devenir la référence sur le marché des robots conversationnels.
Au sein de notre activité Intelligence Artificielle, pour appuyer ses innovations constantes concernant l'identification automatique des sentiments et émotions au sein d'interactions conversationnelles téléphoniques, nous recrutons un Annotateur/Transcripteur H/F :
Ses missions principales :
ANNOTER avec exactitude les échanges entre un client et son conseiller selon des balises expliquées sur un guide,
travailler avec minutie à partir de documents audio et texte en français,
se familiariser rapidement avec un logiciel d'annotation dédié,
connaître les outils de travail collaboratif,
utiliser ses connaissances culturelles, langagières et grammaticales pour rendre compte avec une grande précision non seulement de la conversation entre deux interlocuteurs sur un sujet donné mais aussi de la segmentation de leurs propos.
Le profil du candidat :
être locuteur natif et avoir une orthographe irréprochable,
avoir une très bonne maîtrise des environnements Mac OU Windows OU Linux, - faire preuve de rigueur, d?écoute et de discrétion.
Contrat en CDD (temps complet ou partiel), basé à Paris (75017)
Si intéressé(e), prière de contacter Anne le Gentil/RRH à l?adresse suivante : alegentil@zaion.ai en joignant au mail un C.V
(2019-12-02) 2 postes d'enseignant-chercheur, Université Paris-Saclay, France
2 postes d'enseignant-chercheur (un PR et un MC) vont être mis au concours par l'Université Paris-Saclay en section 27 lors du concours de 2020, avec des profils en Traitement des Langues, dont la Parole en priorité et une recherche qui se fera au LIMSI.
(2019-12-03) Ph studentships, University of Glasgow, UK
The School of Computing Science at the University of Glasgow is offering studentships and excellence bursaries for PhD study. The following sources of funding are available:
* College of Science and Engineering Scholarship: open to all applicants (UK, EU and International) - covers fees and living expenses
* Centre for Doctoral Training in Socially Intelligent Artificial Agents: open to UK or EU applicants who have lived in the UK for at least 3 years through a national competition – see https://socialcdt.org
* China Scholarship Council Scholarship nominations: open to Chinese applicants – covers fees and living expenses
* Excellence Bursaries: full fee discount for UK/EU applicants; partial discount for international applicants
* Further scholarships (contact potential supervisor for details): open to UK or EU applicants
Whilst the above funding is open to students in all areas of computing science, applications in the area of Human-Computer Interaction are welcomed.
Please find below a list of Available supervisors in HCI and their research areas.
* Prof Alessandro Vinciarelli (http://www.dcs.gla.ac.uk/vincia/): Social Signal Processing. Email: Alessandro.Vinciarelli@glasgow.ac.uk * Dr Mary Ellen Foster (http://www.dcs.gla.ac.uk/~mefoster/): Social Robotics, Conversational Interaction, Natural Language Generation. Email: MaryEllen.Foster@glasgow.ac.uk * Dr Euan Freeman (http://euanfreeman.co.uk/): Interaction Techniques, Haptics, Gestures, Pervasive Displays. Email: Euan.Freeman@glasgow.ac.uk
* Dr Fani Deligianni (http://fdeligianni.site/): Characterising uncertainty, eye-tracking, EEG, bimanual teleoperations. Email: fadelgr@gmail.com
* Dr Helen C. Purchase (http://www.dcs.gla.ac.uk/~hcp/): Visual Communication, Information Visualisation, Visual Aesthetics. Email: Helen.Purchase@glasgow.ac.uk
* Dr Mohamed Khamis (http://mkhamis.com/): Human-centered Security and Privacy, Eye Tracking and Gaze-based Interaction, Interactive Displays. Email: Mohamed.Khamis@glasgow.ac.uk
(2019-12-07) Assistant-e ingénieur-e en production, LPL, Aix en Provence, France
Emploi-type :
Assistant-e ingénieur-e en production, traitement de données et enquêtes BAP D (Donnée en SHS) :
Mission :
Au sein de plateforme expérimentale du Laboratoire Parole et Langage (LPL), l'agent sera chargé de la coordination technique, de l'accueil et du soutien aux expériences en collaboration avec les responsables de secteur (audio-vidéo, articulographie/physiologie, neurophysiologie/eye-tracking).
Activités :
Accueillir et recueillir des informations personnelles relatives aux participants dans le respect de la législation en vigueur (RGPD) Assurer le recrutement des participants aux expériences Interfacer avec les chercheurs extérieurs Suivre et renouveler les consommables Assurer la réservation des espaces expérimentaux et des matériels, établissement du planning de passation, prises de rendez-vous Soutenir la mise en place du dispositif expérimental en lien avec le responsable de secteur Renseignement des cahiers de laboratoire Assurer les Campagnes permanentes pour la recherche de volontaires Participer à la rédaction de notices méthodologiques des opérations réalisées Actualiser ses connaissances disciplinaires et méthodologiques et répertorier la bibliographie consacrée à un champ d'études
Compétences :
Maîtrise des techniques, méthodes, et protocoles expérimentaux en SHS. Connaissance dans le domaine de la mesure et des statistiques Travail en collaboration avec les chercheurs dans la conception, la mise en place et la réalisation des expériences Travail en équipe avec les autres personnels ITA intervenant sur la plateforme. Sens aigu des relations humaines dans ses interactions avec des investigateurs aux compétences variées (des étudiants de master aux chercheurs étrangers en passant par les chercheurs et doctorants du laboratoire) et avec toutes les catégories de participants, des enfants d'âge scolaire aux adultes et personnes âgées, et dont certains peuvent présenter différentes pathologies. Connaissance et respect de la législation dans le domaine des recherches sur la personne humaine ainsi que les règles en matière d'hygiène et de sécurité. Bonne maîtrise de l'anglais parlé (Niveau B2 selon le cadre européen de référence pour les langues) se montrera indispensable Archivage pérenne de données de recherche (notion)
La campagne est ouverte jusqu'au 17 janvier mais l'examen des candidatures se fera au fil de l'eau. N'hésitez pas à diffuser cette information auprès des personnes potentiellement concernées.
Starting job date (desired): March 2020. ================================================================== ## Work description
###Project Summary
Automation and optimisation of *verbal interactions of a socially-competent robot*, guided by its *multimodal perceptions*
Facing a steady increase in the ageing population and the prevalence of chronic diseases, social robots are promising tools to include in the health care system. Yet extant assistive robots are not well suited to such context as their communication abilities cannot handle social spaces (several meters and group of persons) but rather face-to-face individual interactions in quiet environments. In order to overcome these limitations and eventually aiming at natural man-robot interaction, the objectives of the work will be multifold.
First and foremost we intend to leverage the rich information available with audio and visual flows of data coming from humans to extract verbal and non-verbal features. These features will be used to enhance the robot's decision-making ability such that it can smoothly take speech turns and switch from interaction with a group of people to face-to-face dialogue and back. Secondly online and continual learning of the advanced system will be investigated.
Outcomes of the project will be implemented onto a commercially available social robot (most likely a Pepper) and validated with several in-situ use cases. A large-scale data collection will complement in-situ tests to fuel further researches. Essential competencies to address our overall objectives lie in dialogue systems / NLP, yet knowledges in vision and robotics would also be necessary. And in any case good command of deep learning techniques and tools is mandatory (including reinforcement learning for dialogue strategy training).
### Requirements
- Master or PhD in Computer Science, Machine Learning, Computational Linguistics, Mathematics, Engineering or related fields - Expertise in NLP / Dialog systems. Strong knowledge of current NLP / Interactive / Speech techniques is expected. Previous experience with dialogue and interaction and/or vision data is a strong plus. - Knowledge in Vision and/or Robotics are plusses. ? Strong programming skills, Python/C++ programmer of DNN models (preferably with pytorch) - Expertise in Unix environments - Good spoken and written command of English is required. *French is optional.* - Good writing skills, as evidenced through publications at top venues (e.g., ACL, EMNLP, SigDial etc) is a plus, for post-doc.
## Place
Bordered by the left bank of the Rhône Avignon is one of the most beautiful city in Provence, for some time capital of Christendom in the Middle Ages. The important remains of a past rich in history give the city its unique atmosphere: dozens of churches and chapels, the ?Palais des Papes? (palace of the popes) the most important gothic palace in Europe), the Saint-Benezet brigde, called the « pont d?Avignon » of worldwide fame through its commemoration by the song, and the ramparts that still encircle the entire city, ten museums from then ancient times to contemporary art.
The 94,787 inhabitants of the city, about 12,000 live in the ancient town centre surrounded by its medieval ramparts. Avignon is not only the birthplace of the most prestigious festival of contemporary theatre, European Capital of Culture in 2000, but also the largest city and capital of the département of Vaucluse. The region offers a high quality of urban life at comparatively still modest costs. In addition to this, the region of Avignon also offers the opportunity to visit numerous monuments and natural beauty sites easily accessible in a very short time: Avignon is the ideal destination for visiting Provence.
The position carries no direct teaching load, but if desired, teaching BSc or MSc level courses is a possibility (paid extra hours), as is supervision of student dissertation projects.
Initial employment is 12 months, extension is possible. For engineer, shift to a PhD position is possible.
## Applications
No deadline: applications are possible until the position is filled.
* Statement of research interests that motivates your application * CV, including the list of publications if any * Scans of transcripts and academic degree certificates * MSc/PhD dissertation and/or any other writing samples * Coding samples or links to your contributions to public code repositories, if any * Names, affiliations, and contact details of up to three people who can provide reference letters for you
(2019-12-08) PhD sudentship, Utrecht University, The Netherlands
The Social and Affective Computing group at the Utrecht University Department of Information and Computing Sciences is looking for a PhD candidate to conduct research on explainable and accountable affective computing for mental healthcare scenarios. The five-year position includes 70% research time and 30% teaching time. The post presents an excellent opportunity to develop an academic profile as a competent researcher and able teacher.
Affective computing has great potential for clinician support systems, but it needs to produce insightful, explainable, and accountable results. Cross-corpus and cross-task generalization of approaches, as well as efficient and effective ways of leveraging multimodality are some of the main challenges in the field. Furthermore, data are scarce, and class-imbalance is expected. While addressing these issues, precision needs to be complemented by interpretability. Potential investigation areas include for example depression, bipolar disorder, and dementia.
The PhD candidate is expected to bridge the research efforts in cross-corpus, cross-task multimodal affect recognition with explainable/accountable machine learning for the aim of efficient, effective and interpretable predictions on a data-scarce and sensitive target problem. The candidate is also expected to be involved in teaching activities within the department of Information and Computing Sciences. Teaching activities may include supporting senior teaching staff, conducting tutorials, and supervising student projects and theses. These activities will contribute to the development of the candidate's didactic skills.
We are looking for candidates with:
a Master?s degree in computer science/engineering, mathematics, and/or fields related to the project focus;
interest or experience with processing of audio/acoustics, vision/video or natural language;
interest or experience with machine learning, affective computing, information fusion, multimodal interaction;
demonstrable coding skills in high-level scripting languages such as MATLAB, python or R;
excellent English oral and writing skills.
The ideal candidate should express a strong interest in research in affective computing and teaching within the ICS department. The Department finds gender balance specifically and diversity in a broader sense very important; therefore women are especially encouraged to apply. Applicants are encouraged to mention any personal circumstances that need to be taken into account in their evaluation, for example parental leave or military service.
We offer an exciting opportunity to contribute to an ambitious and international education programme with highly motivated students and to conduct your own research project at a renowned research university. You will receive appropriate training, personal supervision, and guidance for both your research and teaching tasks, which will provide an excellent start to an academic career.
The candidate is offered a position for five years (1.0 FTE). The gross salary starts at ?2,325 and increases to ?2,972 (scale P according to the Collective Labour Agreement Dutch Universities) per month for a full-time employment. Salaries are supplemented with a holiday bonus of 8% and a year-end bonus of 8.3% per year. In addition, Utrecht University offers excellent secondary conditions, including an attractive retirement scheme, (partly paid) parental leave and flexible employment conditions (multiple choice model). More information about working at Utrecht University can be found here.
Application deadline is 01.01.2020.
Further information and application procedure can be found here.
(2019-12-15) PhD grant at the University of Glasgow, Scotland, UK
The School of Computing Science at the University of Glasgow is offering studentships and excellence bursaries for PhD study. The following sources of funding are available:
* EPSRC DTA awards: open to UK or EU applicants who have lived in the UK for at least 3 years (see https://epsrc.ukri.org/skills/students/help/eligibility/) - covers fees and living expenses * College of Science and Engineering Scholarship: open to all applicants (UK, EU and International) - covers fees and living expenses * Centre for Doctoral Training in Socially Intelligent Artificial Agents: open to UK or EU applicants who have lived in the UK for at least 3 years through a national competition – see https://socialcdt.org * China Scholarship Council Scholarship nominations: open to Chinese applicants – covers fees and living expenses * Excellence Bursaries: full fee discount for UK/EU applicants; partial discount for international applicants * Further scholarships (contact potential supervisor for details): open to UK or EU applicants
Whilst the above funding is open to students in all areas of computing science, applications in the area of Human-Computer Interaction are welcomed.
Please find below a list of Available supervisors in HCI and their research areas.
Available supervisors and their research topics:
* Prof Stephen Brewster (http://mig.dcs.gla.ac.uk/): Multimodal Interaction, MR/AR/VR, Haptic feedback. Email: Stephen.Brewster@glasgow.ac.uk * Prof Matthew Chalmers (https://www.gla.ac.uk/schools/computing/staff/matthewchalmers/): mobile and ubiquitous computing, focusing on ethical systems design and healthcare applications. Email: Matthew.Chalmers@glasgow.ac.uk * Prof Alessandro Vinciarelli (http://www.dcs.gla.ac.uk/vincia/): Social Signal Processing. Email: Alessandro.Vinciarelli@glasgow.ac.uk * Dr Mary Ellen Foster (http://www.dcs.gla.ac.uk/~mefoster/): Social Robotics, Conversational Interaction, Natural Language Generation. Email: MaryEllen.Foster@glasgow.ac.uk * Dr Euan Freeman (http://euanfreeman.co.uk/): Interaction Techniques, Haptics, Gestures, Pervasive Displays. Email: Euan.Freeman@glasgow.ac.uk * Dr Fani Deligianni (http://fdeligianni.site/): Characterising uncertainty, eye-tracking, EEG, bimanual teleoperations. Email: fadelgr@gmail.com * Dr Helen C. Purchase (http://www.dcs.gla.ac.uk/~hcp/): Visual Communication, Information Visualisation, Visual Aesthetics. Email: Helen.Purchase@glasgow.ac.uk * Dr John Williamson (https://www.johnhw.com/): Probabilistic user interfaces, Bayesian interaction, motion correlation interfaces, rich and robust human sensing systems. Email: johnh.williamson@glasgow.ac.uk * Dr Mohamed Khamis (http://mkhamis.com/): Human-centered Security and Privacy, Eye Tracking and Gaze-based Interaction, Interactive Displays. Email: Mohamed.Khamis@glasgow.ac.uk
(2019-12-19) Postdoc at Bielefeld University, Germany
The Faculty of Linguistics and Literary Studies at Bielefeld University offers a full-time
research position (postdoctoral position, E13 TV-L, non-permanent) in phonetics
The Faculty of Linguistics and Literary Studies offers a full time post-doctoral position in phonetics for 3 years (German pay scale: E13).
The Bielefeld phonetics group is well known for its research on phenomena in spontaneous interaction, prosody, multimodal speech and spoken human-machine interaction. Bielefeld campus offers a wide range of options for intra and interdisciplinary networking, and further qualification.
Your responsibilities:
- conduct independent research in phonetics, with a visible focus on modeling or speech technology (65%).
- teach 2 classes (3 hours=4 teaching units/week) per semester in the degree offered by the linguistics department, including the supervision of BA and MA theses and conducting exams (25%).
- organizational tasks that are part of the self-administration of the university (10%).
Necessary qualifications:
- a Masters degree in a relevant discipline (e.g., phonetics, linguistics, computer science, computational linguistics)
- a doctoral degree in a relevant discipline
- a research focus in phonetics or speech technology
- state-of-the-art knowledge in statistical methods or programming skills
- knowledge in generating and analyzing speech data with state-of-the-art tools
- publications
- teaching experience
- a co-operative and team oriented attitude
- an interest in spontaneous, interactive, potentially multimodal data
Preferable qualifications:
- experience in the acquisition of third party funding
Remuneration
Salary will be paid according to Remuneration level 13 of the Wage Agreement for Public Service in the Federal States (TV-L). As stipulated in § 2 (1) sentence 1 of the WissZeitVG (fixed-term employment), the contract will end after three years, In accordance with the provisions of the WissZeitVG and the Agreement on Satisfactory Conditions of Employment, the length of contract may differ in individual cases. The employment is designed to encourage further academic qualification. In principle, these full-time position may be changed into a part-time position, as long as this does not conflict with official needs.
Bielefeld University is particularly committed to equal opportunities and the career development of its employees. It offers attractive internal and external training and further training programmes. Employees have the opportunity to use a variety of health, counselling, and prevention programmes. Bielefeld University places great importance on a work-family balance for all its employees.
Application Procedure
For full consideration, your application should be received via either post (see postal address below) or email (a single PDF) document sent to alexandra.kenter@uni-bielefeld.de by January 8th, 2020. Please mark your application with the identification code: wiss19299. To apply, please provide the usual documents (CV including information about your academic education and degrees, professional experience, publications, conference contributions, and further relevant skills and abilities). The application can be written in German or English.
(2019-12-22) Postdoctoral Researcher, University of Toulouse Jean Jaures, France
Postdoctoral Researcher ? Psycholinguistics, neurolinguistics, corpus linguistics, clinical linguistics Full-Time Position, Fixed-term 1 year (with possibility of one year extension) Application deadline: 05/01/2020 Starting date : 01/02/2020 (flexible)
The Octogone-Lordat Lab (University of Toulouse Jean Jaurès, France : https://octogone.univ-tlse2.fr/) offers a post-doctoral position for 1 year, with a possibility of 1 year extension.
The neuropsycholinguistic study of language processing is the major topic of our lab, focusing on typical language use, language disorders and rehabilitation processes (as in aphasia), at the intersection of linguistics, psycholinguistics and neuroscience.
The post-doc will contribute to the project ?Aphasia, Discourse Analysis and Interactions? funded by the European Regional Development Fund and the Région Occitanie - France. Strong background in linguistics, psycholinguistics or neurolinguistics, cognitive science, and in methodological skills for data collection in corpus linguistics and clinical linguistics as well are required. The post-doc will actively contribute to the development of a new database focusing on typical and atypical language in aphasia. Along with the project supervisors, the post-doc will be involved in all activities in line with the project (e. g. IRB approval, GDPR conformity, etc.), including data collection, coding and analyses from various perspectives. Attested experience with empirical and experimental methods (corpus linguistics) is appreciated, as well as a strong research interest for clinical issues. The post-doc will also coordinate trainees? and students? work involved in the project, and contribute significantly to publication of the findings. The applicant should, at least, have completed a PhD in Linguistics, Neuropsychology, Cognitive Science or related fields, and prove high proficiency level in French according to the CEFRL. Good skills in spoken and written academic English are also required.
This is a full time position starting in February 2020 (flexible). Gross annual salary : min. ?28,000 to ?32,000 (before 15% to 25% taxes and social-security deduction, INM 528 to 564 in accordance with public sector pay scale)
The application should include a CV, a statement of motivations, a link to the PhD thesis, PhD Viva report (if available), plus 2 scanned letters of recommendation.
Deadline for application : 05/01/2020, to : Dr. Halima Sahraoui, sahraoui@univ-tlse2.fr Prof. Barbara Köpke, bkopke@univ-tlse2.fr
For further questions and application submission, please feel free to contact us.
Octogone-Lordat (EA 4156) https://octogone.univ-tlse2.fr/ Université de Toulouse 2 Maison de la Recherche ? E126 5, Allées Antonio Machado 31058 Toulouse Cedex 9 France
About city life in Toulouse : https://www.toulouse-visit.com/
Coreference resolution aims at detecting chains of coreference mentions in a text, that is mentions in the text that refer to the same entity. While at first coreference resolution was split into two separated sub-problems, i.e. mention detections and resolution of coreferent mentions [1], thanks to the development of sophisticated neural models [2,3,4], end-to-end coreference resolution system can be based on a whole single model. The aim of this stage is to study Sequence-to-Sequence [5] and Transformer [6] neural models for coreference resolution, integrating different types of attention mechanisms and possibly arbitrarily-long context [8], with the goal of understanding their impact in dealing with this complex NLP problem.
In this internship the student will implement parts of the systems for coreference resolution with Sequence-to-Sequence and Transformer neural models. The student will run experiments on his own using GPUs, and the systems will be tested on the CoNLL Semeval 2012 benchmark [7].
Profile: - Student for internship level stage (Master 2) in computer science, or from engineering school - Computer science skills: Python programming with good knowledge of deep learning libraries (TensorFlow or PyTorch) Textual data manipulation (xml format, tabular format, CoNLL format) - Interested in Natural Language Processing - Skills in machine learning for probabilistic models
[1] Vincent Ng Supervised noun phrase coreference research: The first fifteen years. Proceedings of ACL, 2010
[2] Sam Wiseman, Alexander M. Rush, Stuart M. Shieber Learning Global Features for Coreference Resolution Proceedings of NAACL-HLT, 2016
[3] Kenton Leey, Luheng Hey, Mike Lewisz, and Luke Zettlemoyer End-to-end Neural Coreference Resolution Proceedings of EMNLP, 2017
[4] Kenton Lee Luheng He Luke Zettlemoyer Higher-order Coreference Resolution with Coarse-to-fine Inference Proceedings of NAACL, 2018
[5] Ilya Sutskever, Oriol Vinyals, Quoc V. Le Sequence to Sequence Learning with Neural Networks Proceedings of NIPS, 2014
[6] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin Attention Is All You Need Proceedings of NIPS, 2017
[7] Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, Yuchen Zhang Conll-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes Proceedings of EMNLP and CoNLL-Shared Task, 2012
[8] Zhang, Jiacheng, et al. 'Improving the Transformer Translation Model with Document-Level Context.? EMNLP 2018.
(2020-01-10) Pre-Doc RESEARCH CONTRACT (for 3 months, extendable to one year), University odf the Basque Country, Leioa (Bizkaia), Spain
One Pre-Doc RESEARCH CONTRACT (for 3 months, extendable to one year) is open for the study, development, integration and evaluation of machine learning software tools, and the production of language resources for Automatic Speech Recognition (ASR) tasks.
Applications are welcome for one graduate (Pre-Doc) research contract for the study, development, integration and evaluation of machine learning software tools, and the production of language resources for ASR tasks. The contract will be funded by an Excellence Group Grant by the Government of the Basque Country. Initially, the contract is for 3 months but, if performance is satisfactory, it will be extended at least to one year ?or even more, depending on the available budget?, with a gross salary of around 30.000 euros/year. The workplace is located in the Faculty of Science and Technology (ZTF/FCT) of the University of the Basque Country (UPV/EHU) in Leioa (Bizkaia), Spain.
PROFILE
We seek graduate (Pre-Doc) candidates with a genuine interest in computer science and speech technology. It will be required knowledge and skills in any (preferably all) of the following topics: machine learning (specifically deep learning), programming in Python, Java and/or C++ and signal processing. A master's degree in scientific and/or technological disciplines (especially computer science, artificial intelligence, machine learning and/or signal processing) will be highly valued. All candidates are expected to have excellent analysis and abstraction skills. Experience and interest in dataset construction will be also a plus.
RESEARCH ENVIRONMENT
The Faculty of Science and Technology (ZTF/FCT) of the University of the Basque Country (https://www.ehu.eus/es/web/ztf-fct) is a very active and highly productive academic centre, with nearly 400 professors, around 350 pre-doc and post-doc researchers and more than 2500 students distributed in 9 degrees.
The research work will be carried out at the Department of Electricity and Electronics of ZTF/FCT in the Leioa Campus of UPV/EHU. The research group hosting the contract (GTTS, http://gtts.ehu.es) has deep expertise in speech processing applications (ASR, speaker recognition, spoken language recognition, spoken term detection, etc.) and language resource design and collection. If the candidate is interested in pursuing a research career, the contract would be compatible with master studies on the topics mentioned above or even a Ph.D. Thesis project within our research group, and further financing options (grants, other projects) could be explored.
The nearby city of Bilbao has become an international destination, with the Guggenheim Bilbao Museum as its main attractor. Still, though sparkling with visitors from worldwide, Bilbao is a peaceful, very enjoyable medium-size city with plenty of services and leisure options, and mild weather, not so rainy as the evergreen hills surrounding the city might suggest.
APPLICATION
Applications including the candidate's CV and a letter of motivation (at most 1 page) explaining their interest in this position and how their education and skills fit the profile should be sent by e-mail ?using the subject 'GTTS research contract APPLICATION ref. 1/2020'? to Germán Bordel (german.bordel@ehu.eus) by Wednesday, January 29, 2020. The contract will start as soon as the position is filled.
