Engineer / Postdoc Position: Deep Learning-Driver Risk prediction from telematics data

Dear colleagues, Please kindly forward the Engineer / Postdoc Position below to potentially interested students (full proposal attached)

Engineer / Postdoc Position: Deep Learning-Driver Risk prediction from telematics data at Telecom SudParis / Institut Polyetchnique de Paris 19 place Marguerite Perey 91120 Palaiseau, France

Please send CV, Motivation Letter, Degrees, Transcripts, and Reference Letters (if any) to :

- Mounîm A. El Yacoubi : [email protected]

Job: CDD, Thesis or postdoc subject, Popcon project (Operational population of knowledge bases and neural networks), LIG (Grenoble)

Thesis or postdoctoral subject within the framework of the Popcorn project (collaborative project with two companies) supervised by Benjamin Lecouteux, Gilles Sérasset and Didier Schwab (Laboratory d’Informatique de Grenoble, Translation Study Group Automatic/Automated Language and Speech Processing)

Title : Operational Population 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 obtain a breakthrough innovation in the field of Treatment Natural Language Automation (TALN) for security and defence, the project focuses on the treatment of French (even if the approaches adopted will then be generalizable to other languages). The work will address different aspects:

- Automatic annotation of textual documents by detection of 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, equipment reference number, etc.), and relationships between entities (a person works for a organization, people involved in an event, ...). Particular attention will be given to being able to adapt flexibly to evolutions of the ontology, the taking into account of the place of the user and the analyst for the validation/capitalization of the extractions carried out.

The project focuses on the following three areas of research: - Generation of synthetic textual data from texts of reference; - Recognition of entities of interest, associated attributes and relationships between entities. - Semantic disambiguation of entities (in case of homonymy by example)

Required profile: - Solid experience in programming & machine learning for the Automatic Language Processing (TAL), including learning deep - Master/Doctorate Machine Learning or computer science, a component TAL or computational linguistics will be a plus - Good knowledge of French

Practical details: - Beginning of the thesis in 2022 - Full-time doctoral contract at LIG (Getalp team) for 3 years (salary: min 1768€ gross monthly) - or Full-time postdoctoral contract at LIG (Getalp team) for 20 months (salary: min 2395€ gross monthly)

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

The means to carry out the (post)doctorate will be provided both in with regard to missions in France and abroad and with regard to concerns the hardware (personal computer, access to the GPU servers of the LIG, Jean Zay computing grid of the CNRS).

How to apply? To apply for a doctoral thesis, candidates must be holders of a Master's degree in computer science, machine learning or automatic natural language processing (obtained before the start of the doctoral contract, students currently in master 2 can thus to apply).

To apply for a postdoctoral fellowship, candidates must be holders of a doctoral thesis in computer science, learning machine 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 must have a good knowledge of learning methods automatic and ideally experience in collecting and managing corpus. They must also have a good knowledge of the language French.

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

PSL University's ACSS Institute Recruits Data Science Engineers for Social Sciences

As part of the development of the ACSS Institute, PSL University is recruiting two Design Engineers (IE) and one Research Engineer (IR) in Data Science. They will be responsible for implementing methods and tools for collecting and processing data from various sources (Web, institutional databases, archives, etc.). They will also be responsible for ensuring compliance with best practices in code and data development and management. Finally, they will contribute to the development of statistical or machine learning models (especially in the field of automated natural language processing).

Created within the University of Paris Sciences et Lettres (PSL) and hosted at Paris Dauphine, the "Applied Computational Social Sciences" Institute aims to strengthen research on major societal issues (political and social cohesion, ecological transition, digital transformation, efficiency and economic competitiveness) by articulating data sciences and social sciences.

The Institute collects and processes heterogeneous data on a large scale both to enable scientific advances and to help inform public debate and decision-making. It brings together a multidisciplinary team of researchers and relies on a team of engineers who bring their expertise to build original databases and operate complex treatments. These projects are initiated and supported by laboratories from the CNRS, Dauphine, ENS, INSP, and MinesParis-Tech. The results of the work are intended to be widely disseminated to institutional partners and the economic world.

Application procedure: The application form is available at this address: https://acss-dig.psl.eu/ candidate. It must include a CV, a cover letter and the statement of the last diplomas.

The review of applications will begin on April 4, 2022 and will continue until the positions are filled.

Postdoc position at MISTEA, INRAE Montpellier, France – Semantic Web, Data linking Areas: Semantic Web, Linked Data, Data linking, Representation learning

Qualifications: PhD in Informatics, AI. Background in knowledge engineering.

Context: ANR DACE-DL (DAta-CEntric AI-driven Data Linking)

Contact & Collaboration: Dan Symeonidou, [email protected]

Clement Jonquet, [email protected]

Dates: Position available for 2 years. Beginning date is flexible.

Location: INRAE, Centre Occitanie-Montpellier, MISTEA research unit

Salary: Between 2200€ and 2700€ gross monthly depending on qualifications and situation.

Institut: INRAE is the French research organization in agriculture, food and environmental sciences; it is a pioneer in France in terms of data sharing and Open Science commitment. The MathNum research department gathers around 200 scientists in mathematics and digital technologies in 13 research units in France. MISTEA is a joint research unit of INRAE and Montpellier Institut Agro engineering school with activities in the development of mathematical, statistical and informatics methods dedicated to analysis and decision support for agronomy and environment. The team is also recognized for its expertise in knowledge engineering and ontology-based scientific data management and information systems.

Project context: Data linking is the scientific challenge of automatically establishing typed links between the entities of two or more structured datasets. A variety of complex data linking systems exists, evaluated on public benchmarks [1,2,3]. While they have allowed for the generation of vast amounts of linked data in the context of various dedicated projects, data generic systems often have limited applicability in many real-world scenarios, where data are highly heterogeneous and domain-specific. The ANR project DACE-DL (2022-2024) targets a paradigm shift in the data linking field with a data-centric bottom-up methodology relying on machine learning and representation learning models [4]. We hypothesize there exists a finite number of identifiable and generalisable linking problem types (LPTs), that we need to categorize and analyze to provide better linking results.

Topic: The postdoc will work to identify and provide a categorisation/taxonomy of the different linking problem types based on an in-depth analysis of the linked datasets provided by the project and beyond. The first objective is to provide an in-depth analysis of the linked data available along with an exhaustive study of the state-of-the-art in the field of data linking. A finite number of generalisable linking problem types will be classified including the relations and inherent structure of the LPTs made explicit to both human and machine. The goal is to answer questions such as: are certain LPTs or groups of LPTs (e.g. siblings at a given level of the taxonomy) specific to a domain, language or a community? Are certain LPTs inherent to specific types of data? Once a formal taxonomy of LPTs is produced, various datasets will be manually annotated. These annotations on existing pairs of datasets will be used to learn, using machine learning strategies, features for the automatic categorization of other datasets. The postdoc will co-supervise a PhD student working on the machine learning methods.

Application: Send application to the contact emails including: a short description of introducing yourself your adequacy to the position a CV and one major publication

PhD THESE PROPOSAL on "Discovery of Strategic Knowledge in the Web of Data" (click to access the topic)

Summary: The Web of Data is already widely used in information retrieval. Its knowledge graphs should also be a source of strategic knowledge to support knowledge workers such as journalists or specialists in the humanities and social sciences. In particular, a major challenge to help them is to identify socio-economic indicators aimed at quantifying phenomena and their impact within a field. Unfortunately, the diversity of entities and their links in the graphs of the Web of Data makes it difficult to develop automated and transdisciplinary methods to identify these indicators, which must be specific to each discipline. In addition to being heterogeneous, graphs are numerous, voluminous and distributed, raising the issue of parsimonious algorithms to be able to scale. The objective of this thesis work is to propose transdisciplinary models and to implement them in the knowledge graphs of the Web in order to automatically discover interpretable indicators within their socio-economic field. The main expected contributions are: the proposal of ontology-sensitive data analysis models, the proposal of algorithms for generating socio-economic indicators and the development of an online prototype following the FAIR principles.

keywords: Semantic Web, Linked Open Data, Wikidata, knowledge graphs, ontologies, knowledge extraction, data analysis

Laboratory: LIFAT, BdTln team, University of Tours Location: university branch of Blois Duration: 3 years Funding: Centre Val de Loire Region

Candidate profile: Master in Computer Science, introduction to research (teaching followed, or project, or internship), motivation for the semantic web and knowledge extraction

Link file in ADUM : linkDate limit complete file in ADUM : 06/05/2022

Contacts: beatrice.markhoff@univ-tours. en and [email protected]

Topic ----- The advent of deep learning has been a real tsunami in the machine learning community, leading to results, especially in computer vision, that we would not have expected a few years before. For many vision tasks, the performances of deep learning algorithms have become equivalent or even superior to human performances.

However, these results have been obtained at the cost of ever-increasing use of resources such as: the size of the model, the time and energy needed to train them, with ever-larger databases and ever-higher annotation requirements. This increase in resource requirements has major drawbacks, related to the impact that ML has on the environment, the difficulty to implement models on embedded architectures, or the challenges raised when models have to be trained on tasks for which little training data is available.

These observations have very recently led some authors [1, 2] to introduce the concept of frugal machine learning and to define what a frugal machine learning methodology should be, and how to evaluate frugality.

In this dissertation, we will study frugality in the context of AI for image segmentation [3]. The objective will be to propose frugal models that can provide efficient results while being structured to provide a reduced time and space complexity. More precisely, we will consider several aspects of frugality and take inspiration from the following recent works : i) the conception of lightweight models by design [4, 5] ii) the compression of existing models [6] iii) the pruning of existing segmentation models [4, 7] iv) frugality on image label and zero shot image segmentation [8, 9].

Candidates must have an MSc or engineering degree in a field related to computer science, electrical engineering, or applied mathematics, with strong programming skills (in particular with deep learning frameworks). Experience with image processing will be a plus. Candidates are expected to have abilities to write scientific reports and communicate research results at conferences in English.

The position is starting as soon as possible with a salary of 32 kEuros gross, and will be located in Caen, France.

Applications should include the following documents in electronic format: i) A short motivation letter stating why you are interested in this project, ii) A detailed CV describing your past research background related to the position iii) The transcripts for master degrees. iv) The contact information for three references (do not include the reference letters with your applications as we will only ask for the reference letters for short-listed candidates).

Please send your application package to [email protected] and [email protected]

Ideally located in the heart of Normandy, two hours from Paris and just 10 minutes away from the beaches, Caen, William the Conqueror’s hometown, is a lively and dynamic city.

A pdf version of the position is available at https://lezoray.users.greyc. fr/tmp/PhD_FrugalAI.pdf

Good evening everyone. We are opening 3 post-docs/cdds to contribute to our ExpressIF AI (https://expressif.cea.fr).

Keywords: fuzzy logic, possibilities, learning.

3 different fields of application: health, materials and safety.

*Context* The world of financial services has long been one of the forerunners of digital innovation through the computerization of its processes, tools and services. The recent period has seen the emergence of a number of new technologies that are changing practices, including Blockchain technology, which has the potential to completely reshape financial services in the world of corporate and investment banking (e.g. market issuance, financing, risk management and hedging activities).

*Thesis topic* In this context, BNP Paribas CIB in collaboration with Paris-Dauphine University is exploring the opportunities and impacts of tokenization related to Blockchain technology. Some expertise has already been acquired based on concrete developments, taking into account technological, legal and regulatory aspects. However, there is still a large field of uncertainties related to this technology, and in particular the interoperability between the different protocols. At this stage, it is clear that technological consolidation has not taken place, and probably will not take place for several years, and in some areas only. For example, the tokenization of "cash" (via central players for example) and the tokenization of financial securities will probably be on these different technological bases. In the same way, the management of public spaces (e.g. Ethereum) and private spaces will be instrumental in the ability to manage financial value in this new world.

The work will therefore focus mainly on technological interoperability, whether via the various languages of smart contracts, cryptography techniques, the management of "incentives" of an ecosystem, tokenomics etc. in order to ensure the digital uniqueness from one universe to another in a secure and irrefutable way.

The orientations given to the thesis can be chosen through the prism of research interests and their scientifically innovative character, the candidate's appetites, via a discussion with the CIFRE supervisor and the thesis director.

*Skills required* You are a graduate of a bac + 5 and wish to move towards a PhD in Computer Science. You also have an appetite for new technologies and digital levers. You are fluent in French and English and you are proficient in a Java-type development language. Knowledge of the basics of cryptography, Blockchain technologies (layer 1 and/or 2) as well as the most common smart contract languages (e.g. Solidity for Ethereum/Polygon, SmartPy for Tezos, DAML, Hyperledger Fabric...), interoperability technologies such as Cosmos or Polkadot are a plus.

Your adaptability and ability to collaborate are essential assets. Add to this your ability to communicate, your ability to synthesize as well as your rigor to finish convincing us.

Finally, we attach particular importance to ensuring that our future employees act on a daily basis with ethical and professional responsibility.

In a changing world, diversity, equity and inclusion are key values for team well-being and performance. At BNP Paribas, we want to welcome and retain all talents without distinction: this is how we will build, together, the innovative, responsible and sustainable finance of tomorrow.

At any time during the recruitment process, the information on your CV and your identification data may be verified by an external service provider mandated by BNP Paribas.

*Additional information for recruitment* - Recruitment date: from September 2022

- Remuneration: competitive at the level of a BNP consultant.

- Funding: CIFRE BNP Paribas CIB

- Environnement thesis scholarship, a BNP team well established in research and development that includes a dozen Cifre doctoral students.

- Academic laboratory: LAMSADE, Paris Dauphine University - PSL

*Application* Interested persons are invited to send their CV, statements from the last three years (and this year if applicable), as well as the cover letter to Daniela Grigori (daniela.grigori@lamsade. dauphine.fr) and Khalid Belhajjame ([email protected])

Postdoc position at MISTEA, INRAE Montpellier, France Semantic Web, Data linking

*Areas:* Semantic Web, Linked Data, Data linking, Representation learning

*Qualifications:* PhD in Informatics, AI. Background in knowledge engineering.

*Context:* ANR DACE-DL (DAta-CEntric AI-driven Data Linking) https://anr.fr/Projet-ANR-21-CE23-0019

*Contact & Collaboration:* *Danai Symeonidou, [email protected]* *Clement Jonquet, [email protected]*

*Dates:Position available for 2 years. Beginning date is flexible. *

*Location:INRAE, Centre Occitanie-Montpellier, MISTEA research unit* https://www6.montpellier.inrae.fr/mistea/

*Salary:* Between 2200€ and 2700€ gross monthly depending on qualifications and situation.

*Institut:* INRAE is the French research organization in agriculture, food and environmental sciences; it is a pioneer in France in terms of data sharing and Open Science commitment. The MathNum research department gathers around 200 scientists in mathematics and digital technologies in 13 research units in France. MISTEA is a joint research unit of INRAE and Montpellier Institut Agro engineering school with activities in the development of mathematical, statistical and informatics methods dedicated to analysis and decision support for agronomy and environment. The team is also recognized for its expertise in knowledge engineering and ontology-based scientific data management and information systems.

*Project context:* Data linking is the scientific challenge of automatically establishing typed links between the entities of two or more structured datasets. A variety of complex data linking systems exists, evaluated on public benchmarks [1,2,3]. While they have allowed for the generation of vast amounts of linked data in the context of various dedicated projects, data generic systems often have limited applicability in many real-world scenarios, where data are highly heterogeneous and domain-specific. The ANR project DACE-DL (2022-2024) targets a paradigm shift in the data linking field with a data-centric bottom-up methodology relying on machine learning and representation learning models [4]. We hypothesize there exists a finite number of identifiable and generalisable linking problem types (LPTs), that we need to categorize and analyze to provide better linking results.

*Topic:* The postdoc will work to identify and provide a categorisation/taxonomy of the different linking problem types based on an in-depth analysis of the linked datasets provided by the project and beyond. The first objective is to provide an in-depth analysis of the linked data available along with an exhaustive study of the state-of-the-art in the field of data linking. A finite number of generalisable linking problem types will be classified including the relations and inherent structure of the LPTs made explicit to both human and machine. The goal is to answer questions such as: are certain LPTs or groups of LPTs (e.g. siblings at a given level of the taxonomy) specific to a domain, language or a community? Are certain LPTs inherent to specific types of data? Once a formal taxonomy of LPTs is produced, various datasets will be manually annotated. These annotations on existing pairs of datasets will be used to learn, using machine learning strategies, features for the automatic categorization of other datasets. The postdoc will co-supervise a PhD student working on the machine learning methods.

*Application: Send application to the contact emails including:*

- *a short description of introducing yourself *

- *your adequacy to the position *

- *a CV and *

- *one major publication*

Thesis proposal: Point cloud based large-scale place recognition - Application to the prevention against fake news

Full text in English: https://www.umr-lastig.fr/vgouet/News/annonce_these_PlaceReco3D_2022-EN.pdf

Subject of the thesis The thesis project focuses on 3D point cloud based large-scale place recognition, with the application of geolocation of 3D image data. Without any extra information of the initial position, geolocalazing image content relies on the indexing and retrieval of content similarities in a geolocalized reference. This thesis proposes to study this type of approach by exploiting 3D maps based on acquisition campaigns (in particular LiDAR) that are becoming mainstream thanks to high quality geometry reconstruction which makes them attractive, but also complex to handle given their volume and diversity. Please consult the full text in PDF for the description of the subject thesis.

Context The fields of application of place recognition from images are numerous, we will deal here with the case of the geolocation of amateur video sequences as a certification tool for the prevention against fake news. Massively spread on social networks and on the web, amateur videos relaying information or an event are now very important, with among them content that is fake news, i.e. taken outside of its original context, to express bad or false information. To fight against this form of misinformation, several media, such as the French public television channel “France TV”, have set up a fact checking unit of images and videos which analyzes, verifies and certifies these streams. This complex work is done by hand and would benefit from being automated by using artificial intelligence tools. The verification of geolocation was recognized as essential to best explain what is happening. It is in this collaborative context between IGN and France TV that we focus on this geolocation criterion with the desire to exploit the best georeferencing repositories of today to offer automatic large-scale geolocation solutions, which can, among other things, contribute to the fact checking of visual information.

Candidate profile Bac+5 in computer science, applied mathematics or geomatics (master or engineering school). A good background in machine learning is required, and a knowledge on 3D computer vision or image indexing will be appreciated. The successful candidate must have good programming skills (Python, C/C++). Although fluency in French is not required, fluency in English is necessary. Curiosity, open-mindedness, creativity, perseverance and the ability to work in a team are also key personal skills in demand.

Please note the only students from the European Union, the United Kingdom or Switzerland are eligible for this thesis project.

Organization Start: last quarter of 2022

Place: the thesis will be carried out in Paris area at the LaSTIG laboratory, located in Saint-Mandé (73 avenue de Paris, Saint-Mandé metro, line 1) in the premises of the IGN. The doctoral student will be attached to the MSTIC Doctoral School (ED 532).

The French mapping agency IGN (National Institute for Geographic and Forest Information) is a public administrative establishment attached to the French Ministry of Ecological Transition; it is the national reference operator for mapping the French territory. The LaSTIG Laboratory in Sciences and Technologies of Geographic Information for the smart city and sustainable territories, is a joint research unit attached to the Gustave Eiffel University, the IGN and the School of Engineering of the city of Paris (EIVP). It is a unique research structure in France and even in Europe, bringing together around 80 researchers, who cover the entire life cycle of geographic or spatial data, from its acquisition to its visualization, including its modeling, integration and analysis; among them about thirty researchers work in image analysis, computer vision, machine learning, photogrammetry and remote sensing. LaSTIG researchers can be involved in the teaching activities of the IGN engineering school, the ENSG (Ecole Nationale des Sciences Géographiques), which offers access to undergraduate and graduate students with excellent quality in fields related to geographic information sciences: geodesy, photogrammetry, computer vision, remote sensing, spatial analysis, cartography, etc.

How to apply Before March 28, 2022, please send both contacts in a single PDF file the following documents:

- A detailed CV

- A topic-focused cover letter

- Grades and ranks over the last 3 years of study

- The contact details of 2 referents who can recommend you

Contacts - Laurent Caraffa – [email protected], Researcher at LaSTIG (thesis supervisor), IGN, Gustave Eiffel University

- Valérie Gouet-Brunet – [email protected], Research director at LaSTIG (director of the thesis), IGN, Gustave Eiffel University

The University of New Caledonia is recruiting, for the needs of its Science and Technology Department and as of 1 July 2022, a lecturer in the CNU 27 (Computer Science) section by delegation.

This profile is intended only for tenured teacher-researchers.

Attached job description

The application files (cover letter, CV, copy of the last promotion and assignment order, and copy of the identity document) must be sent, in pdf format, no later than February 28, 2022 electronically to [email protected]

The young company EasyChain and the L3i research laboratory in La Rochelle are launching a call for applications for a CIFRE doctoral position in the field of conversational agent development.

Details of the offer are available at This address: Dynamic Human-Agent Interactions adapted to users' profiles.

Le.la successful candidate must hold a master's degree or equivalent degree in computer science or automatic language processing. A strong background in machine learning and good communication in English are required.

The thesis will take place mainly in Niort at EasyChain, in a French-speaking environment.

If you are interested in this position, please send the following information to Antoine Doucet ([email protected]), and Ahmed Hamdi ([email protected]):

* Detailed CV * Bachelor's and Master's degrees.

* Letters of recommendation

Applications will be considered until March 1, 2022.

Three MCF positions (section 27) are open for competition at LIMOS, UMR CNRS 6158 (https://limos.isima.fr), Clermont Auvergne University:

1- Profile "Data, Services, Intelligence", teaching assignment: ISIMA, Clermont Auvergne INP (https://www.isima.fr/)

2- Profile "Models and Algorithms of Decision Support", teaching assignment: ISIMA, Clermont Auvergne INP (https://www.isima.fr/)

3- Profile "Data sciences for engineering and industrial systems", teaching assignment: SIGMA'Clermont, Clermont Auvergne INP (https://www.sigma.fr/)

You will find attached the profiles of the three positions.

The latter are also available at this address: https://limos.fr (section: job offers)

A position of lecturer in section 27 is open at the University of Paris Panthéon Assas.

Courses in computer science mainly concern training in economics and management. They range from office automation in L1 eco-management to more specific courses in master such as data mining, through programming courses in Python, VBA, SQL or R.

Teaching contact: Maria Rifqi ([email protected])

With regard to research, the future candidate will be integrated into the CRED laboratory (http://cred.u-paris2.fr): Centre for Research in Economics and Law. The preferred research topics, without being limiting, are: algorithmic game theory, decision theory, blockchains.

Research contact: Bertrand Crettez ([email protected])

The link to the post on Galaxie:

https://www.galaxie. teachingup-research. gouv.fr/ensup/ ListesPostesPublies/ANTEE/2022_1/0751718K/FOPC_0751718K_4317.pdf

Please share this announcement with anyone potentially interested.

The Institute of Communication of the University of Lyon 2 is recruiting a part-time contract research professor for the next academic year (PAST), in the field of computer science and data science. To apply for this type of position, it is necessary to be able to justify a main professional activity of at least 900 hours.

All information is available here: https://icom.univ-lyon2.fr/ institut/news/recruitment-past

Do not hesitate to contact Julien JACQUES for more information: [email protected]

Deadline 31 mars 2022.

Hello, IRISA's EXPRESSION team is launching a call for applications for a Doctoral position in the field of multimodal detection of deep fakes.

Details of the offer are available at:

MUDEEFA - MUltimodal DeEEp Fake detection using Text-To-Speech Synthesis, Voice Conversion and Lips Reading https://www-expression.irisa.f r/files/2022/03/MUDEEFA_2022_e n.pdf

The candidate will be required to conduct applied research of leads in one or more of the following areas: signal processing, statistical machine learning, speech and gesture recognition.

He/she will have excellent computer programming skills (e.g. C/C++, Python/Perl, etc.), and knowledge of machine learning, signal processing or human-computer interaction.

The position requires a Master's degree in Computer Science or an Engineering Degree giving the title of Master's degree in Computer Science.

The thesis will take place in Lannion, in the Côtes d'Armor, within the EXPRESSION team.

Please send a detailed CV, a cover letter, one or more letters of reference and the academic results of the previous degree (Master or Engineer giving the title of Master) to all contacts indicated in the subject before Friday, April 8, 2022, strict limit

We are looking for a PhD student for a thesis offer at Inria Lille, co-supervised by Antoine Amarilli and myself, on explanation and circuits for the evaluation of requests and logical reasoning.

The objective of the thesis is to study in detail the problems related to the explanation of logical reasoning made in several frameworks: the explanation of querying databases by queries, the inference of data by logical rules and the execution of programs defined by logical rules such as data-centric workflows.

The application link and more information are available on the Inria portal https://jobs.inria.fr/public/c lassic/en/offers/2022-04484 and the deadline to apply is 8 April. If you know students currently looking for a thesis, do not hesitate to send them this proposal!

Pierre Bourhis CNRS Researcher, SPIRALS Team, UMR 9189 CRIStAL, INRIA Lille