LITIS (Laboratoire d’Informatique, Traitement de l’information et des Systèmes) is a research laboratory associated to the University of Rouen Normandie, Le Havre Normandie Normandie, and School of Engineering INSA Rouen Normandie.

Research at LITIS is organized around 7 research teams which contribute to 3 main application domains: Access to Information, Biomedical Information Processing, Ambient Intelligence. LITIS currently includes 90 faculty staff members, 50 PhD students, 20 PostDoc and Research Engineers.

The Machine Learning team of LITIS is developing research in modeling unstructured data (signals, images, text, etc...) with machine learning algorithms and statistical models. For more than two decades it has contributed to the development of reading systems and document image analysis for various applications such as postal automation, business document exchange, digital libraries, etc...

EXO-POPP project: Optical Extraction of Handwritten Named Entities for Marriage Certificates for the Population of Paris (1880–1940)

Thanks to a collaboration between specialists in machine learning and historians, the EXO-POPP project will develop a database of 300,000 marriage certificates from Paris and its suburbs between 1880 and 1940. These marriage certificates provide a wealth of information about the bride and groom, their parents, and their marriage witnesses, that will be analyzed from a host of new angles made possible by the new dataset.

These studies of marriage, divorce, kinship, and social networks covering a span 60 years will also intersect with transversal issues such as gender, class, and origin. The geolocation of data will provide a rare opportunity to work on places and relocations within the city, and linkage with two other databases will make it possible to follow people from birth to death. Building such a database by hand would take at least 50,000 hours of work.

But, thanks to the recent developments in deep learning and machine learning, it is now possible to build huge databases with automated reading systems including handwriting recognition and natural language understanding. Indeed, because of these recent advances, optical printed named entity recognition (OP-NER) is now performing very well.

On the other hand, while handwriting recognition by machine has become a reality, also thanks to deep learning, optical handwritten named entity recognition (OH-NER) has not received much attention. OH-NER is expected to achieve promising results on handwritten marriage certificates dating from 1880 to 1923. This project research questions will focus on the best strategies for word disambiguation for handwritten named entity recognition.

We will explore end-to-end deep learning architectures for OH-NER, writer adaptation of the recognition system, and named entity disambiguation by exploiting the French mortality database (INSEE) and the French POPP database. An additional benefit of this study is that a unique and very large dataset of handwritten material for named entity recognition will be built.

Laboratoire LITIS, EA 4108, Université de Rouen, 76 800 Saint-Etienne du Rouvray, FRANCE

Téléphone : (33) 2 32 95 50 13 Fax : (33) 2 32 95 50 22 Email : [email protected]

Missions The research engineer will be in charge of the development of a processing pipeline dedicated to optical printed named entity recognition (OP-NER). He will closely collaborate with a Ph.D. student in charge of Handwritten Named Entity Recognition (OH-NER).

OP-NER is the project’s easiest task and will benefit from the latest results achieved by the LITIS team on similar problems on financial yearbooks. Images are first processed to extract every text information.

This will be achieved with the DAN architecture designed by LITIS which is a deep- learning-based OCR (https://arxiv.org/abs/2203.12273). The research engineer will be in charge of this OCR task.

A benchmark of DAN against available OCR software such as Tesseract and EasyOCR will also be conducted. Then the textual transcriptions will be processed for named entity extraction and recognition.

Named entity recognition is a well-defined task in the natural language processing community. In the EXO-POPP context however, we need to define each entity to be extracted more precisely to make a clear distinction between the different people occurring in the text. For example, we need to distinguish between wife and husband names, and similarly for the parents of the husband and of the wife, and so on for the witnesses, children, etc. An estimation of around 135 categories has been established.

The TAG definition was made by LITIS as well as a first training dataset. Manually tagging the transcriptions has been made possible through the PIVAN web-based collaborative interface (https://litis-exopopp.univ-rouen.fr/collection/12 ). This platform provides in one single web interface a document image viewer, viewing and editing of OCR results and text tagging facilities for NER.

PIVAN eases the annotation efforts of the H&SS trainees and allows for building the large, annotated datasets required for machine learning algorithms to run optimally.

The research engineer will oversee datasets generation and curation as per the requirement of the EXO-POPP NER task, including the handwritten datasets. The named entity recognition task will be based on a state-of-the-art machine learning approach.

We have started some experimentations with the well-known FLAIR NER library (https://github.com/flairNLP/flair). We plan to continue developing and tuning the EXO-POPP named entity recognition module using this library. The research engineer will oversee this task entirely.

Tasks • The research engineer will be in charge of tuning PIVAN for the OCR task. A benchmark of DAN with the available OCR technologies such as Tesseract and EasyOCR will also be conducted.

• The research engineer will be in charge of datasets generation and curation as per the requirement of the EXO-POPP NER task, including the handwritten datasets.

• The research engineer will be in charge of developing the NER module.

Laboratoire LITIS, EA 4108, Université de Rouen, 76 800 Saint-Etienne du Rouvray, FRANCE

Téléphone : (33) 2 32 95 50 13 Fax : (33) 2 32 95 50 22 Email : [email protected]

Deliverables: Transcription of the typescript corpus Named entities extracted from the typescript corpus

Skills : • General software development and engineering, Python • Machine Learning, Computer vision, Natural Language Processing • Ability to work in a team, curious and rigorous spirit • Knowledge in web-based programming is a plus

Position to be filled : Positions: 1 Research Engineer

Time commitment: Full-time

Duration of the contract: September 1st 2022 – 31st August 2023

Contact: Prof. Thierry Paquet, [email protected]

Indicative salary: €24 000 annual net salary, plus French social security benefits

Location: LITIS, Campus du Madrillet, Faculty of science, Saint Etienne du Rouvray, France

Title: Design and Development of a Virtual Platform for Animal Experimentation

Research Laboratory: Laboratoire d’Informatique de l’Université du Mans (LIUM) - Computer Science Lab of Le Mans University

Technology Enhanced Learning Team

Location of the Post-Doctorate: IUT de Laval – Bâtiment CERIUM2, 52 rue des docteurs Calmette et Guérin, 53020 Laval, France

Supervisor: Lahcen Oubahssi, Associate Professor in Computer Science

Contact: Lahcen Oubahssi ([email protected])

Financing: Virtual3R Project (Virtual Reality platfoRm for animal expeRimentation)

Dates: From 1st October 2022 to 30th September 2023 (12 months)

Salary: The postdoc will earn 2849 € (gross) per month.

Keywords: Virtual Reality, Animal Experimentation, Pedagogical simulation, Virtual Learning Environments, Technoplogy Enhanced Learning, 3R.

Context of the post-doctoral subject: Virtual3R project

Research on the animal model for scientific purposes is still essential today to protect the health of citizens and animals, and to preserve the environment.

Many medical, veterinary, pharmaceutical, toxicological studies, etc, all ethically validated, use the animal model, and could not be carried out on another model. These studies are governed by legislation and regulations. European and French regulations aim to reduce the number of experiments on animals used for scientific purposes.

They encourage the development of alternative methods and the use of the animal model only in the absence of other methods available to meet the object of the study. In general, these regulations are based on the 3Rs principle, consisting of reducing, replacing and refining (improving) as much as possible the use of animals.

The Virtual3R project aims to offer an alternative and complementary method based on virtual reality to reduce the number of animals used in animal experimentation training while allowing students to acquire a good understanding of basic technical procedures and gestures before experimentation with real animals.

Indeed, virtual reality offers new experiences to users through ever more efficient possibilities for interaction and immersion. These possibilities are of great interest in the learning domain.

TEL (Technology Enhanced Learning) research has shown great interest in virtual reality technology because of its ability to simulate a wide diversity of real-world conditions. In this context, we are interested in VRLE (Virtual Reality Environments for Human Learning) which aim to offer learners virtual educational situations.

The TEL-LIUM team has developed its expertise around the design and operationalization of learning situations in a virtual reality context (Oubahssi and Piau-Toffolon 2019) (Mahdi et al. 2019) (Mahdi 2021) (Oubahssi and Mahdi 2021).

Our latest research work has provided the first solution for structuring VR-oriented educational situations in the form of reusable scenario models in different learning contexts (Mahdi 2021) (Oubahssi and Mahdi 2021). As part of this project (Virtual3R) we would like to:

• Study the question of modeling the adaptation of virtual educational situations via the reusability of virtual educational objects

• Improve the existing prototype (VEA tool) by proposing a new virtual platform for animal experimentation whose objective is to meet the new needs of the biological department teachers/ students (Laval Institute of Technology, France).

Project tasks In the Virtual3R project context, the candidate will be particularly involved in the following tasks:

• Participating in research work on the modeling and adaptation of virtual educational situations via the reusability of virtual educational objects

• Participating in the design and development of the new Virual3R platform allowing the integration and organisation of the various educational activities related to animal experimentation

• Testing and validating the results with the biological department teachers and students (Laval Institute of Technology, France).

Candidate profile & required skills We are looking for a researcher who holds a PhD in the field of Computer Science, with experience in Technology Enhanced Learning and /Or Virtual Reality.

The candidate must: • have defended his PhD less than 3 years before the beginning of the Postdoctorate (after 1st October 2019).

• have advanced knowledge in Unity / C# development • have good writing skills • have relational and teamwork skills • be autonomous, diligent and rigorous.

The candidate is not required to speak French although it would make it easier to communicate with the local teachers during the co-design and testing phases.

How to apply? Applicants should send their application files before August 30th 2022, to Lahcen Oubahssi ([email protected]).

Application files should contain at least a full Curriculum Vitae including a complete list of publications, a cover letter indicating their research interests, achievements to date and vision for the future, as well as either support letters or the name of 2 persons that have worked with them.

We have several doctoral grants in the QARMA machine learning team of the University of Aix Marseille, in particular on:

* Machine Learning and deep Learning for Astrophysics Collaboration with the LAM (Laboratoire d'astrophysique de Marseille)

* Deep Learning for understanding sound representations in the brain Collaboration with the INT (Institut de Neurosciences de la Timone)

More details on these two interdisciplinary topics can be found here: https://qarma.lis-lab.fr/ open-positions/phd-offers-starting-september-2022/

PhD position at IRIT (Toulouse, France)

Topic - Transformer-based retrieval models for structured and verbose queries

Starting date: Fall 2022

Research team: IRIS at IRIT : Lynda Tamine / José G Moreno / Taoufiq Dkaki Synergy: Christophe Thovex

Profile: - Master's level or engineering school in Computer Science, with skills in Information Extraction/Research and Text Mining - Good English skills (written and oral)

- Good skills in advanced programming (Python, Pytorch, sklearn, ...) - Good knowledge in Machine Learning, deep learning is a plus

Funding: Total gross salary for 3 years : 105 401,50 € / Note that final monthly salary depends of personal situation in France

Application instructions: All applications must include the following to be considered: detailed CV, cover letter, transcripts (with rankings), contacts for recommendation. Please use “PhD application - Synergie” as the subject of the email. Applications to be sent by mail to Lynda Tamine, José G Moreno, and Taoufiq Dkaki ([email protected], [email protected], [email protected]).

All applications will be processed as they arise until the positions will be filled.

Location: Institut de Recherche en Informatique de Toulouse (IRIT) University of Toulouse 3 Paul Sabatier (UT3) 118 Route de Narbonne, F-31062 TOULOUSE CEDEX 9, France

Comment: Nantes (France) is also an alternative location if requested by the applicant as Synergie has also offices in Nantes.

We are looking for an engineer for at least one year to work with the open source ReservoirPy library on health data.

You will be working in Bordeaux, France in the Mnemosyne computational neuroscience group https://team.inria.fr/mnemosyne in collaboration with other Bordeaux teams involved in biostatistics and machine learning.

More info and online application: https://jobs.inria.fr/public/classic/en/offres

* ReservoirPy ReservoirPy [1, 2, 3] is a simple user-friendly library based on Python scientific modules. It provides a flexible interface to implement efficient Reservoir Computing (RC) [5] architectures with a particular focus on Echo State Networks (ESN) [4]. Advanced features of ReservoirPy allow to improve computation time efficiency on a simple laptop compared to basic Python implementation. Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters with the help of the hyperopt library. It includes several tutorials exploring exotic architectures and examples of scientific papers reproduction.

Github: https://github.com/reservoirpy/reservoirpy Documentation: https://reservoirpy. readthedocs.io Related projects: https://github.com/reservoirpy Twitter: https://twitter.com/reservoirpy

* Deadline: 20th of September 2022 For informal questions send an email to xavier dot hinaut at inria dot fr. Online application: https://jobs.inria.fr/public/ classic/en/offers

* Contract Engineer contract of 1 year, with possibility to extend the contract to more years or to continue in the team as PhD student.

* Benefits package - Subsidized meals - Partial reimbursement of public transport costs - Possibility of partial teleworking and flexible organization of working hours - Professional equipment available (videoconferencing, loan of computer equipment, etc.)

- Social, cultural and sports events and activities - Access to vocational training - Social security coverage

The Demotal platform (https://www.demotal.fr/), proposed by the Association of Language Industries Professionals (APIL), popularizes NLP solutions to users, both general public and industrial, through case studies.

Each case study presents the need as it can be expressed by a user, the types of possible solutions, and offers pointers to technical information.

The Demotal platform, announced during the days on multlinguism organised within the framework of the French Presidency of the Council of the European Union, receives the support of the Ministry of Culture.

APIL is looking for a web editor to write case studies of the Demotal platform.

You will ensure the collection of information in the literature or by interview with professionals, then the writing for the web of the "need" and "solution" sections. Your posts will be proofread and validated before publication on the platform.

Paid in copyright, this activity will be for you the opportunity to meet industrial players in the field. This activity can be conducted remotely.

You are a position or student in a TAL program, want additional remuneration and prepare your integration into the language industries sector .

You have good writing skills, great rigor and the spirit of synthesis,

Thank you for contacting us: [email protected]

OPérationnel settlement of knowledge bases and Neural Networks

The project addresses the problem of semi-automated enrichment of a knowledge base through automatic text analysis.

In order to achieve a breakthrough innovation in the field of Natural Language Processing (NLP) for security and defense customers, the project focuses on the processing of French (even if the approaches chosen will subsequently be generalizable to other languages). The work will address different aspects:

The automatic annotation of textual documents by detecting mentions of entities present in the knowledge base and their semantic disambiguation (polysemy, homonymy); The discovery of new entities (people, organizations, equipment, events, places), their attributes (age of a person, reference number of a piece of equipment, etc.), and relationships between entities (a person works for an organization, people involved in an event, ...).

Particular attention will be given to the fact of being able to adapt flexibly to changes in ontology, taking into account the place of the user and the analyst for the validation/capitalization of the extractions carried out.

The project focuses on the following three research axes:

Generation of textual synthetic data from reference texts; Recognition of entities of interest, associated attributes , and relationships between entities. The semantic disambiguation of entities (in case of homonymy for example)

Profile sought:

- Solid experience in programming & machine learning for Automatic Language Processing (NLP), including deep learning

- Master/PhD Machine Learning or computer science, a TAL or computational linguistic component will be a plus appreciated - Good knowledge of French

Practical details: - Start of the thesis or PostDoc as soon as possible

- Full-time doctoral contract at the LIG (Getalp team) for 3 years (salary: min 1768€ gross monthly) or Full-time postdoctoral contract at the LIG (Getalp team) for 24 months (salary: min 2395€ gross monthly)

Scientific environment:

The doctorate or postdoctoral fellowshipled within the Getalp team of the LIG laboratory (https://lig-getalp.imag.fr/). The person recruited will be welcomed into the team which offers a stimulating, multinational and pleasant working environment.

The means to carry out the (post)doctorate will be ensured both with regard to missions in France and abroad and with regard to equipment (personal computer, access to the GPU servers of the LIG (https://lig-getalp.imag.fr/encyclopedia/access-to-servers/), Jean Zay calculation grid of the CNRS, (https://lig-getalp.imag.fr/encyclopedia/jean-zay/) ).

How do I apply?

To apply for a doctoral thesis, candidates must hold a Master's degree in Computer Science, Machine Learning or Natural Language Processing (obtained before the start of the doctoral contract).

To apply for a postdoctoral fellowship, candidates must hold a doctoral thesis in computer science, machine learning or natural language processing (obtained before the start of the doctoral contract, students whose defense is scheduled before the end of September 2022 can thus apply).

They should have a good knowledge of machine learning methods and ideally experience in corpus collection and management.

They must also have a good knowledge of the French language .

Applications must contain: CV + cover letter/message + master's notes + letter(s) of recommendations; and be addressed to Benjamin Lecouteux ([email protected]), Gilles Sérasset ([email protected]) and Didier Schwab ([email protected])

This Post-doc will be done in the ConfianceAI framework (Confiance.ai). Confiance.ai is the technological pillar of the Grand Défi “Securing, certifying and enhancing the reliability of systems based on artificial intelligence” launched by the French Innovation Council.

It is the largest technological research programme in the #AIforHumanity plan, which is designed to make France one of the leading countries in artificial intelligence (AI). This post-doc aims to design models to estimate and predict CNN performances without requiring their deployment on the real platform.

These models will save time since measurement campaigns will be needed.

Furthermore, it will be possible to test CNN architectures and measure their performances, in terms of execution time, power consumption, memory occupation, etc, before the platforms being available in the market.

Thus, measurements such as execution time, or energy consumption can be estimated on an architecture not available from the manufacturer if we know its most "impacting" attributes on the Hardware architecture(s).

This solution makes it possible to quickly explore a large space of configurations (CNN architectures, hardware architectures).

It also makes it possible to avoid developing and optimizing models that will turn out ineffective on the target platform. In this postdoc we will develop new models for CNN performance prediction that respect the rank of the models (Pareto rank-preserving surrogate models).

Hardware-aware Neural Architecture Search (HW-NAS) has recently gained steam by automating the design of efficient DL models.

However, such algorithms require excessive computational resources and thousands of GPU days are needed to evaluate and explore CNN search space. In this Postdoc we will explore the effectiveness of multi-objective Surrogate Performance Prediction Models in HW-NAS.

Tasks that have to be developed in this postdoc are : - Determination of the CNN search space - Determination of the main characteristics for two of Hardware platforms. This may concern Qualcomm Hexagon DSPs and edge Nvidia GPUs.

- Development and testing of multi-objective surrogate performance prediction models for these 2 types of HW platforms.

Education: A Ph.D in computer/electrical science/engineering is required

Salary: 2300 euros/month net (3000 euros/month gross)

Deadline for application: 30/09/2022

Duration : 1 year (1 year extension possible)

Employer : IRT Systemx

Address: 2 Bd Thomas Gobert, 91120 Palaiseau Start date: Preferably between Sept 1st 2022 and Nov 1th 2022.

An application prepared in English or French should contain:

1. CV with the list of publications. 2. Contact information for 2-3 reference persons. 3. Your most relevant conference or journal publications, in full-text.

For further information contact: - Prof. Smail Niar ([email protected]) LAMIH/CNRS, INSA Hauts-de-France and CNRS, France - Prof Elghazali Talbi ([email protected]), INRIA and University of Lille, France

Key words: machine learning on time series, clustering, classification, average time series

Context Surgical robotics is now widely used with, for instance, more than 5000 Da Vinci systems and one million procedures performed worldwide. Surgery is a complex activity, in a very small anatomical volume, and with a lot of variability between patients and between surgeons. The global objective of the two-year SPARS (Sequential Pattern Analysis in Robotic Surgery: Understanding Surgery) project led by the MediCIS team (LTSI (1), Inserm, Rennes 1 University) is to develop data analysis approaches being able to provide a better understanding of the surgical practice, from complex surgical data. The approaches will be developed thanks to the complementary skills available in the project’s consortium, including time series analysis. In this consortium, the IRISA laboratory (Rennes and Vannes) is calling for applications for a post-doctoral research position (duration two years) on time series analysis.

(1) Signal and Image Processing Laboratory

Missions In the SPARS project context, a trajectory compiles information on the 3D location of the tip of a surgical instrument at the hands of the surgeon, at a constant frequency. The candidate will be mostly involved in one of the three workpackages of the SPARS project. A first task will focus on clustering and classification for such trajectories. Various practical objectives are pursued, including the generation of a model corresponding to a cluster or a class, the characterization of operating modes specific to a type of patient or a type of surgeon, the provision of advice to practitioners in the case of robotic surgeries that are not or not very well documented, the identification of the level of expertise of a practitioner, the prediction of the surgical procedure to be chosen according to the type of patient. These investigations will use dissimilarity measures based on temporal alignment, as DTW [SC71] or elastic kernels as proposed in [CVB07], [CB17] and [M19a]. This task will also address co-clustering for trajectories. The investigations will focus on how to combine time series with other types of data for a co-clustering purpose, using either deep learning [XCZ19] if enough data is available, symbolic representation [BBC15] or latent block [BLN20] models that all need to be adapted to the specificity of kinematics data.

Once a cluster or a class is obtained, another task will be to compute an average trajectory from a set of trajectories. The practical objectives will be the following: highlight deviations from the average trajectory that are potentially interpretable (as characteristics of the practitioner, or of the patient, for example) ; identify the best operating mode to young practitioners or trainees if it is possible to correlate the operating mode with clinical results. Intuitively, on the graphical representation of a time series, variability related to temporality (phase) concerns the abscissa axis, and variability related to shape concerns the ordinate axis. To compute a consensus trajectory, the second task of the package will examine how to extract the atemporal form and the variable component related to temporality, assuming that this atemporal form may be interpreted as an approximation of the consensus. The problem of shape and phase separation has been studied in [PZ16], [SSV10] and [M19a]. The second task will examine how to improve the preliminary work in [M19b], notably by proposing other kernels.

Requirements for this position Doctorate in computer science, applied mathematics and computer science, or mathematics, with a specialization in machine learning and the following requirements:

- theoretical skills and experience in probability / statistics, applied mathematics, machine learning, - strong knowledge and solid experience in temporal data analysis,

- publications in major conferences or journals in the field, - mastery of data manipulation, relying on machine learning libraries,

- programming experience, good programming skills (notably in Python) and technical ability to manage a code development project, - ability to work in a team, and report on the progress of work.

Some knowledge in deep learning will be a plus.

The personal qualities expected are mostly autonomy and interest in interdisciplinarity (health), as well as writing skills (both in French and English). Fluency in French will be a plus.

Work environment Rental: Institute for Research in Computer Science and Random Systems (IRISA), University of Rennes 1 - Beaulieu Campus, 263 Av. Général Leclerc, 35000 Rennes

Duration: 24 months – Applications will be accepted until the position is filled (for recruitment by 1 December 2022 at the latest)

Host team: LINKMEDIA

The successful candidate will work with four academic researchers from IRISA / Rennes / LINKMEDIA team (Simon Malinowski, Associate Professor in Computer Science), IRISA / Vannes / EXPRESSION team (Pierre-François Marteau, Full Professor in Computer Science), LS2N (2) / Nantes / DUKe team (Christine Sinoquet, Associate Professor with French Accreditation to supervise Research (HdR)) and INSERM / Rennes / LTSI MediCIS team (Pierre Jannin, Directeur de recherche INSERM, HdR). The successful candidate will collaborate with the partners in the project, among which the other post-doctoral fellow involved in the project and the project partners experts in surgery and in surgical data analysis.

(2) Laboratory of Digital Sciences of Nantes: UMR CNRS 6004

Income: 2160,26 euros before taxes monthly

How to apply? Documents to be provided :

- detailed Curriculum Vitae including a complete list of publications, - letter of motivation indicating the candidate’s research interests and achievements to date,

- a selection of publications, - the PhD thesis manuscript,

- Master 2 marks (with rank and number of students in the year) - letters of recommendation for the current year,

- contact details of two referees (at least) with whom the candidate has worked (first name, surname, status, institution (give details of acronyms if applicable), city, e-mail address, telephone number)

Questions or application files (zip archive only) should be sent to the four contact persons below:

[email protected]

[email protected]

[email protected]

[email protected] (SPARS project leader)

Simon Malinowksi http://people.irisa.fr/Simon. Malinowski/

Christine Sinoquet https://christinesinoquet. wixsite.com/christinesinoquet

Pierre-François Marteau https://people.irisa.fr/ Pierre-Francois.Marteau/

Pierre Jannin https://medicis.univ-rennes1. en/members/pierre.jannin/index

Dear colleagues, We have an open Doctoral position in AI for commercial vehicles at Halmstad University (Sweden). It is offered within a newly granted Swedish (Vinnova-funded) project "Big Data-Powered End User Function Development", in collaboration with Volvo Trucks in Gothenburg (https://www.vinnova.se/en/p/big-data-powered-end-user-function-development-big-fun). The thesis will seek to apply AI methods to identify moments of potential significance in truck journeys, building a holistic context of truck usage situations and driver journeys.

The PhD salary in Sweden is very competitive (~2600€/month before taxes in the first year), and Halmstad is a small but well-communicated city, so living costs are smaller than in bigger cities. Big hubs (Copenhagen, Gothenburg) are within 1.5h by train, and Stockholm or Oslo are half-day away.

The deadline is 4 September! (although we will probably extend it). More info about the position and how to apply: https://hh.varbi.com/se/what:job/jobID:533430

Please do not hesitate to apply or distribute this message to potentially interested candidates in your networks.

Fernando Alonso-Fernandez Associate Professor Halmstad University, Sweden

https://sites.google.com/view/ fernando-alonso-fernandez

*PhD position on Data Profiling, Protection and Sharing* PSL, Paris Dauphine University

We have an opening for a PhD position with the objective to develop new solutions to help data providers who wish to share their data to better understand it, and to choose the best-suited data protection policies.

The PhD Student will be investigating techniques for profiling and linking datasets that would help data providers to gain insight into their data, to estimate its (economic) value, and to choose data protection strategies that go beyond privacy protection to take into account the protection of the data provider's economic assets.

The PhD thesis is part of an interdisciplinary project involving another PhD thesis on data governance in the field of management sciences. We anticipate that the interaction between the two doctoral students will lead to interdisciplinary contributions in addition to computer science-focused solutions.

The PhD candidate will work in close collaboration with members of the data science team of the Paris Dauphine University. The problems investigated and solutions developed will be guided and validated within case studies in the fields of health and economics.

The successful candidate will enroll as a PhD student in the Computer Science department of the Paris Dauphine University-PSL(under the co-direction of Khalid Belhajjame and Daniela Grigori) and will become a member of the Data Science team of the same university. Paris Dauphine University is located in the city of Paris, and is a member of PSL (Paris Sciences et Lettres).

We seek strongly motivated candidates prepared to dedicate to high quality research.

The candidate should have (or be close to obtaining) a Master's degree or equivalent in computer science or applied mathematics. Starting date Octomber/2022.

Interested candidates are invited to send the following to [email protected] and daniela.grigori@lamsade. dauphine.fr

- academic CV - academic transcripts of BSc and MSc - one page motivation letter explaining why the candidate is suitable for the position - contact details of two referees

[1] https://dauphine.psl.eu/dauphine/dauphine-numerique

EDA Days website: http://eric.univ-lyon2.fr/eda/

we are looking for an engineer to join the Loki team at Inria Lille, for a 24-month fixed-term contract to be filled by December.

For the job description and to apply: https://jobs.inria.fr/public/classic/en/offers/2022-05321

Please forward in your networks or to people you know who may be interested.

Sincerely, Stéphane Huot

Stéphane Huot Scientific Officer at Inria Lille - Nord Europe Loki research group http://loki.lille.inria.fr/~huot/

Position: Technical Editor Enterprise Konverso

Location: Hybrid Distanciel/ Boulogne-Billancourt, Île-de-France, France

Description: As part of its development, Konverso is recruiting in 2022 a new technical writer

Presentation: Konverso is a publisher of a platform in the field of Conversational AI . The technology core of the platform combines NLP, Machine Learning, Search and automation engine. We graduated from Microsoft's Startup AI program in 2017. Our conversational platform identified in analyst reports (Forrester, Everest Group) allows our customers to deploy multiple use cases : Virtual Agent, Intelligent Triage, Augmented Agent, Sentiment Analysis.

Our clients include major French brands such as Air Liquide, VEOLIA, Colas, Alten, European startups such as Volumio, Equalum. We are also experiencing strong international growth with customers in the US, Germany, Australia, and Switzerland.

Konverso is the winner of innovation awards from the Ministry of Research, collaborates with AI-focused structures such as Quantmetry to advance its technology. Konverso has established major technology partnerships with ServiceNow and Microsoft.

Role In charge of all our product documentation, you will maintain and continuously improve our online documentation. You like to innovate, learn about new technologies and adopt modern documentation techniques, including video tutorials , integrating the documentation contained in the software, etc.

You are proficient in English, which is our documentation language. You will become an expert in our software, who will know more at your fingertips and then you will devote yourself entirely to this transfer of knowledge to our users.

Work environment All activities are carried out within an ITIL change management framework, with the use of the Atlassian Jira tool for ticket and sprint tracking and the Atlassian Confluence wiki for technical and user documentation. Product code is tracked in GIT repositories, with daily branch usage, pull requests with revisions, and approvals by other team members. The work is organized around the Ag methodologyisland, with daily Sprints and stand-ups.

The product team is multidisciplinary, combining Machine Learning, NLP (Natural Language Processing) and development., Each has a well-defined role and carries a great responsibility on its perimeter, with little or no assistance, real expertise, and autonomy required. You will work under the supervision of the CTO in an exciting working environment, with constant innovation, rapid development and publication cycles (close to continuous development), and excellent motivation and team spirit

Skills and knowledge required:

- Perfect written skills in English

- Experience in CMS tools such as Atlassian Confluence

- Video editing capabilities (Camtasia, Adobe Premiere)

- Experience in using ticketing tools such as Jira to read technical notes

- Familiar with the enterprise software lifecycle would be a plus:

- Any past experience in software documentation in the fields of automation, conversation, research, NLP

- Other European language skills (French, German, etc.)

Contact Please send your CV and cover letter Bertrand Lafforgue +33 6 64 40 52 64

We invite applications for a 3-year PhD position co-funded by Inria, the French national research institute in Computer Science and Applied Mathematics, and LexisNexis France, leader of legal information in France and subsidiary of the RELX Group.

The overall objective of this project is to develop an automated system for detecting argumentation structures in French legal decisions, using recent machine learning-based approaches (i.e. deep learning approaches). In the general case, these structures take the form of a directed labeled graph, whose nodes are the elements of the text (propositions or groups of propositions, not necessarily contiguous) which serve as components of the argument, and edges are relations that signal the argumentative connection between them (e.g., support, offensive). By revealing the argumentation structure behind legal decisions, such a system will provide a crucial milestone towards their detailed understanding, their use by legal professionals, and above all contributes to greater transparency of justice.

The main challenges and milestones of this project start with the creation and release of a large-scale dataset of French legal decisions annotated with argumentation structures. To minimize the manual annotation effort, we will resort to semi-supervised and transfer learning techniques to leverage existing argument mining corpora, such as the European Court of Human Rights (ECHR) corpus, as well as annotations already started by LexisNexis. Another promising research direction, which is likely to improve over state-of-the-art approaches, is to better model the dependencies between the different sub-tasks (argument span detection, argument typing, etc.) instead of learning these tasks independently. A third research avenue is to find innovative ways to inject the domain knowledge (in particular the rich legal ontology developed by LexisNexis) to enrich enrich the representations used in these models. Finally, we would like to take advantage of other discourse structures, such as coreference and rhetorical relations, conceived as auxiliary tasks in a multi-tasking architecture.

The successful candidate holds a Master's degree in computational linguistics, natural language processing, machine learning, ideally with prior experience in legal document processing and discourse processing. Furthermore, the candidate will provide strong programming skills, expertise in machine learning approaches and is eager to work at the interplay between academia and industry.

The position is affiliated with the MAGNET [1], a research group at Inria, Lille, which has expertise in Machine Learning and Natural Language Processing, in particular Discourse Processing. The PhD student will also work in close collaboration with the R&D team at LexisNexis France, who will provide their expertise in the legal domain and the data they have collected.

Applications will be considered until the position is filled. However, you are encouraged to apply early as we shall start processing the applications as and when they are received. Applications, written in English or French, should include a brief cover letter with research interests and vision, a CV (including your contact address, work experience, publications), and contact information for at least 2 referees. Applications (and questions) should be sent to Pascal Denis ([email protected]).

The starting date of the position is 1 November 2022 or soon thereafter, for a total of 3 full years.

Please contact [email protected] if interested

The main goal of this PhD topic lies in proposing, training and evaluating a variety of neural computational models that may help us understand the brain representations of sounds. We will focus on deep neural networks as these offer a high flexibility for implementing various hypotheses and will aim at mapping novel DNN architectures’ representation spaces to the cerebral responses to natural sounds measured with magnetoencephalography (MEG).

Integrating constraints to model brain processing of sensory input with deep networks. A number of previous works have already explored the use of deep networks to learn a hierarchy of representation spaces in their hidden layers, that are then mapped to specific brain areas in order to label these areas with e.g. a level of computation corresponding to the depth of the hidden layer that best matches this area [Güçlü et al., 2015], [VanRullen et al., 2019, Güçlü et al., 2016]. A few attempts have been made to leverage the brain data during model training to enforce correlation from intermediate layers activation to specific brain areas activations (e.g. [Federer et al., 2020]).

We want to explore such a direction. Beyond integrating constraints on correlation of brain and computational representations in a deep architecture, we want to build on the knowledge of the structure of the auditory system, as well as recent progress in the mapping of acoustic representations to the brain, to enforce specific hypothesis about the chain of transformations in the brain, by constraining intermediate layers to represent specific acoustic properties of the sound stimuli.

Deep learning models for unsupervised learning of audio source representation. In the proposed PhD, we will build on and extend deep learning methods that have fuelled recent progress in machine learning, but have not yet been applied to the audio domain and/or whose potential to model human brain activity remains to be explored. This may include, in particular, self-supervised learning approaches trained on large recordings of natural sounds, like wavenet auto-regressive models [van den Oord et al. 2016], generative adversarial networks [Donahue et al. 2018], wavenet variational autoencoders [Chorowski et al. 2019]

Another promising approach—already quite successful in the visual domain [Yuan and Peng 2019, Radford et al. 2021, Ramesh et al. 2022] — consists of leveraging widely available natural language supervision— for example under the form of captions associated with audio clips—to learn sound representations. The visual information often associated with audio clips could also be considered as an additional source of cheap and useful supervision [Guzhov et al. 2022, Wang et al. 2021, Sarkar and Etemad 2021].

Applications. The fundamental works described above may have many potential applications, we plan to explore the potential of our results in particular for the synthesis of audio scenes. Networks leveraging natural language supervision may be particularly interesting in this context as natural language prompts may be used to flexibly control the sounds being generated [Yuan and Peng 2019, Gafni et al. 2022, Nichol et al. 2021].

You will find below a thesis offer that will start in November 2022 (CIFRE):

Topic: Learning for Intent Recognition and conversation management

Reception: SyCoSMA team, LIRIS laboratory / Reecall company, Lyon, CIFRE context

Keywords: deep learning, conversational agents, AI, natural language processing (NLP), intent recognition (NLU), few shot learning, active learning, goal-oriented dialog systems

Detailed subject: https://perso.liris.cnrs.fr/frederic.armetta/sujetTheseNLP-2022.pdf

Do not hesitate to contact me for any clarification, or to distribute this offer to anyone who may be interested. Cordially,

Frederic Armetta Web page: http://liris.cnrs.fr/membres?idn=farmetta Email: [email protected] Lyon 1 University - LIRIS Laboratory ( http://liris.cnrs.fr/ ) Nautibus building 8, Boulevard Niels Bohr 69622 Villeurbanne Cedex, France Tel: +33 (0)4.72.43.19.97 Fax: +33 (0)4.72.43.15.37