The Faculty of Management, Economics and Siences (FGES) of the Catholic Institute of Lille and the Institut Mines-Télécom Nord-Europe are joining forces to propose this thesis topic in computer science, applied in mobile robotics .

The objective of this thesis is to implement and evaluate control and decision-making strategy for an autonomous mobile robot working in a medicalized environment in collaboration with healthcare personnel.

(English version: Planning in dynamic environments applied to service robots in nursing homes)

Details: https://arts.wp.imt.fr/2022/04/phd-offer-on-applied-robotics

Start date: September 2022

Location: Douai / Lille

Laboratoire d’accueil / Host Laboratory : FEMTO-ST. DISC Dept. Spécialité du doctorat préparé/Speciality : Computer Science (Informatics) Mots-clefs / Keywords : Deep Learning. Unsupervised. DAM. Enery function. Stable state Descriptif détaillé de la thèse / Job description

The PhD applicant must have good skills in computer science especially deep learning architectures, machine learning frameworks, high-level programming languages, as well as in applied mathematics particularly numerical approximation methods.

Preferred selection criteria: - Very good knowledge of deep learning - Very good-level programmer - Good skills in numerical algorithms

Personal characteristics: - Methodical - Calm

Financement : MESRI Etablissement

Début du contrat : October, 1, 2022

Salaire mensuel brut : 1975€

Direction de la thèse:/ Thesis Supervisor: Prof. Jacques Bahi BAHI Jacques / [email protected]

Encadrement de la thèse : co-directeur(s) et co-encadrant(s)

Co-encadrant : Christophe Guyeux. Professeur

Applicants are invited to submit their application to the PhD supervisors.

Application must contain the following documents: - CV

- Cover letter - At least 1 reference letter

I am looking for a candidate for a postdoc of 12 to 24 months in the field of genetic programming for the analysis of medical images (histopathology). The postdoc will take place at the IRIT (Toulouse, France) in collaboration with the Institut Universitaire en Cancérologie de Toulouse (IUCT). All the details are in the attached file. Do not hesitate to contact me for more information or to apply!

Lecturer - Associate ProfessorUniversité Toulouse 1 CapitoleInstitut de Recherche en Informatique de ToulouseREVA TeamCNRS

http://www.irit.fr/~Sylvain. Cussat-Blanc/index_en.php

Expected competencies

We are looking for candidates graduated in computer science or applied mathematics with skills in evolutionary computation and, if possible, genetic programming. Candidates must have undeniable coding and scientific paper writing experiences.

Skills in image analysis will be appreciated, in particular in the field of medical images.

Candidates must be interested in collaborating with medical doctors as they will be co-supervised by expert in genetic programming and doctor in histopathology (see section “Supervision” above).

Supervision

The candidate will be supervised by:

Sylvain Cussat-Blanc, Hervé Luga & Jean-Marc Alliot, IRIT CNRS UMR5505, experts in evolutionary computation and applications to biomedical images

Camille Franchet & Pierre Brousset, IUCT CHU Toulouse, experts in histopathology

The hired candidate will be work at CRCT in the IRIT-CRCT project team. Computer science and artificial intelligence are taking an increasing place in the world of medical research, and in particular in the world of cancer research.

The joint IRIT/CRCT team, co-located on the Toulouse Oncopole site, aims to have computer scientists, cancer researchers and physicians work together on the same site.

Contract 12-months contract with a possible extension to 12 additional months. Salary will be discussed depending on the experience of the candidates. The postdoc contract can start as soon as possible and will be situated at CRCT, 2 avenue Hubert Curien, 31000 Toulouse, France.

PhD. position in Computer Science Machine learning and data mining with imperfect and incomplete relational data Laboratoire Hubert Curien (LabHC), France

Title: Complex graph data analysis with imperfect / incomplete data

Laboratory: Laboratoire Hubert Curien (LabHC UMR CNRS 5516), Université Jean Monnet, Saint-Étienne, France

Keywords: Data mining, Machine learning, Social network, Graphs analysis, Incomplete / imperfect data

General Overview: In many applications, the data to be studied is relational, modeled in the form of a network represented by graphs. This representation allows capturing not only the information about entities (using attributes or properties) but also the relationships between them.

While the ability to discover knowledge from such network data is gaining in importance, the quality became a central issue in their exploitation. The aim of this PhD is first to study the impact of the lack of quality of relational data on the data mining and machine learning algorithms and, second to design robust methods to deal with imperfect and incomplete relational data and able to provide explainable results

Working environment: The PhD candidate will work at the Laboratoire Hubert Curien (UMR 5516) under the supervision of Baptiste Jeudy, Charlotte Laclau and Christine Largeron, (LabHC – Université Jean Monnet, Saint-Etienne, France).

Funding: The PhD fellowship is funded for 3 years from October 2022 and is monthly funded about approximatively 1500 €.

Profile of the candidate: The candidate should have a master degree or equivalent in Computer Science. The subject is at the intersection of several domains: graph theory, statistics, data mining and machine learning. Thus the candidate should have strong backgrounds in several of these topics.

Other required skills: • Good abilities in algorithm design and programming. • Good technical skills regarding data mining, machine learning and data management

• A very good level (written and oral) in English. • Good communication skills (oral and written). • Ability to work in a team with colleagues, • Autonomy and motivation for research.

Application instructions: Applicants are invited to contact as soon as possible. The application file should contain the following documents:

1. a curriculum vitæ (CV);

2. the official academic transcripts of all the candidate’s higher education degrees (BSc, License, MSc, Master’s degree, Engineer degree, etc.). If the candidate is currently finishing a Master’s degree, s/he must send the transcript of the grades obtained so far, with the rank among her/his peers, and the list of classes taken during the last year;

3. some recommendation letters (quality is more important than quantity, there);

4. and a motivation letter written specifically for this position.

Send all of these documents by email to all the advisors: • Baptiste Jeudy [email protected]

• Charlotte Laclau [email protected]

• Christine Largeron [email protected]

Interviews will be conducted as they arise and the position will be filled as soon as possible

Important remark: This internship is part of an international collaboration between the Alberta Machine Intelligence Institute (AMII) at the University of Alberta in Edmonton and the LabHC notably within the framework of the IEA CODANA supported by the CNRS. As part of the thesis, the doctoral student could stay at the AMII at the University of Alberta in Edmonton (Canada).

Data and software support for robust discourse parsing and its application - Contract duration: 12 months

- Starting date: June 2022 (flexible)

- Location: IRIT, Université P. Sabatier (Toulouse III)

- Remuneration: 2035-2630 euros, gross salary, depending on experience

- Application deadline: the position will be open until fulfilled

- Send application by email to [email protected]

- More information at: https://pagesperso.irit.fr/~Chloe. Braud/andiamo/

*Natural Language Processing *(NLP) is a domain at the frontier of AI, computer science and linguistics, aiming at developing systems able to automatically analyze textual documents. Within NLP, *d**iscourse parsing* is a crucial but challenging task: its goal is to produce structures describing the relationships (e.g. *explanation, contrast*...) between spans of text in full documents, allowing for making inference on their content. Developing high-performing and robust discourse parsers could help to improve downstream applications such as automatic summarization or translation, question-answering, chat bots. However, current performance are still low, mainly due to the lack of annotated data.

In order to develop robust discourse parsers within the *AnDiAMO* project, we want to explore multi-objective settings, where the goal is ultimately to perform a discourse analysis, but relying on another related objective such as performing well on another task (e.g. morphological, syntactic or temporal analysis), or an application (e.g. sentiment analysis or argument mining). We will also explore the issues of cross-language and cross framework learning.

The hired engineer will be in charge of:

Set up evaluation*: set up pipeline systems for evaluation of downstream applications (e.g. sentiment analysis, question-answering, argument mining...) ; investigating different ways of using the discourse parsers outputs to test the impact of discourse information.

Corpus curation*: collect datasets for several tasks (e.g. POS tagging, syntactic parsing, temporality, modality…) and pre-process them ;

Corpus harmonization*: collect existing discourse corpora and harmonize them, following the format used for the DisRPT shared task (https://sites.google.com/georgetown.edu/disrpt2021/home? authuser=0)

The position is funded by the ANR AnDiAMO project, for which postdocs and master interns will also be recruited. Collaborations are planned with researchers in Toulouse, Grenoble, Nancy and Munich. The hired person will be part of the MELODI team at IRIT, participating in team and project meetings, and co-authoring articles.

- Master or PhD degree in computer science or computational linguistics

- Interest in language technology / NLP

The recruited engineer should have good developing skills. Knowledge in machine learning would be a plus. In addition to these tasks, it will be possible to investigate other paths, such as building multi-task learning architectures or testing few-shot learning strategies, according to the interests of the candidate.

Please send a CV and a few lines explaining your interest for the position to [email protected]

A thesis offer on the granular interpretation of heterogeneous and multivariate data is to be provided at the UBFc (Univ. Bourgogne Franche-Comté) and the LIB (Laboratoire d'Informatique de Bourgogne).

Thesis title: Granular interpretation of heterogeneous and multivariate data Host laboratory: LIB - EA 7534, 9 avenue Alain Savary, 21000 Dijon, FRANCE - https://lib.u-bourgogne.fr/

Specialty of the doctoral program prepared: Computer Science

Tagged Artificial intelligence, knowledge modeling, logical rules, machine learning, artificial neural networks, granular computing, data science

Context: As a priority of the French government, the fight against drug trafficking is, on the one hand, a public health issue, with an average of 168,000 deaths each year worldwide directly caused by drug use, and, on the other hand, a security issue, since it leads to a deterioration in living conditions and violence in the affected neighborhoods. The Minister of the Interior has placed, from July 2020, this fight against drugs among his 3 priorities. The knowledge of the products circulating in France is collected in the national database STUPS© (System for the Standardized Treatment of Narcotic Products) of the Ministry of the Interior. This database contains heterogeneous and multivariate data: macroscopic data (e.g. logos, dimensions), qualitative (e.g. names of cutting agents), quantitative data (e.g. active ingredient content), but also non-confidential survey data (e.g. quantities seized, date and place of seizure on French territory). Created in 1986, the STUPS© database is fed by the 5 Forensic Laboratories of the National Forensic Service (SNPS) and by the Criminal Research Institute of the National Gendarmerie, and today contains about 10 million entries. Presented in September 2019, the French Stup Plan provides for a series of 55 measures, including "The implementation of new indicators to know the uses of consumers, the methods of traffickers and anticipate their evolutions". However, the inherent structure of the STUPS© database and the characteristics of the data contained do not make it possible to extract knowledge (interpretation by a machine), in order to be able to identify, explain and predict consumer uses and the methods of traffickers.

Work envisaged: The aim is to propose an intelligent system to meet the challenges related to the interpretation of heterogeneous and multivariate data (linear and non-linear models) contained in the STUPS© database in order to describe, understand and explain implicit knowledge. The research work targeted in this thesis concerns the field of Artificial Intelligence (AI), and will focus on two fundamental aspects: symbolic AI (knowledge models defining semantics – Motik et al., 2012 – and other symbolic aspects to interpret and reason on this knowledge – Motik 2006), on the one hand, and statistical AI (machine learning models such as artificial neural networks – Bishop 1995 – making it possible to build predictions), on the other hand. The planned research will explore the articulation of these AI approaches with granular approaches (Mani 1998). Indeed, according to Hobbs (Hobbs 1985), the ability to conceptualize the world at different levels and to benefit from full mobility between these levels is an exclusive feature of human problem solving. Indeed, when we look at the world around us we only get the things that serve our interests of the moment. As part of this thesis, we will investigate the application of Hobbs' theory of granularity to the constituted knowledge model, in order to allow reasoning at different levels of granularity.

The research issues addressed are: - how to integrate consistently and consistently into a knowledge base (ontology) heterogeneous and inconsistent data over time?

- how to exploit the results obtained from machine learning algorithms to improve the description of knowledge?

- how to interpret and reason on the data thus integrated in order to deduce new knowledge?

- how to maximize the effectiveness of the approach thus specified?

Requested profile: Applicants must hold a degree in Computer Engineering or a Master 2 in Computer Science.

Fluency in the French language is essential (min. C1 level). A good level of English communication is a plus.

Applicants must have an interest in research.

Constitute a plus of the skills in knowledge engineering (Semantic Web, ontologies) and / or data science.

Start and duration (planned): September-October 2022, 36 months

Funding: MESRI institution File to be sent by 28/05/2022

Audition period: over the water

Job description: http://spim.ubfc.fr/wp-content/uploads/sites/17/2022/04/LIB-A-ROXIN-MESRI.pdf (in french and english)

Institut Pascal (Clermont-Ferrand, France) and CREATIS (Lyon, France) are looking for a 12-months postdoc in the field of medical image segmentation using Deep Learning.

The postdoc proposal aims at segmenting left ventricle in 3D from cine-MRI. The main contribution will be to integrate constraints in the form of geometric models into a Deep Learning framework.

More detailed description of the proposal to be found at: https://bit.ly/37X2Jak

Dear all, I am excited to be hiring a postdoc in the space of machine learning and visual computational neuroscience to join my group at the institute of cognitive science (University of Osnabrück, Germany). The full-time position is initially for 3 years, but can be extended.

- Details about the position are provided here: https://www.uni-osnabrueck.de/universitaet/stellenangebote/stellenangebote-detail/75-ikw-postdoc-mfd/

- You can find out more about our work here: https://www. kietzmannlab.org/

- Information about research in Germany more generally: https://twitter. com/TimKietzmann/status/1482027695856828417

Dear all, We are seeking candidates for a PhD position on "Modeling 6DoF Navigation and the Impact of Low Vision in Immersive VR Contexts"

The detailed offer, instructions to apply, as well as application link can be found on this page:

https://recrutement.inria.fr/public/classic/en/offres/2022-04807

The Laboratory of Computer Science and Digital Society (LIST3N) at UTT is recruiting a doctoral student for a doctoral thesis in Computer Science. The subject is attached.

Interested candidates, please send us your application before 03 MAY 2022

Duration: 3 years from October 2022.

Candidate Profiles • Hold a research master's degree in Computer Science, Applied Mathematics or a diploma engineer with research activities.

• Advanced programming capacity (in C, C++, python).

• Very good knowledge of combinatorial optimization tools (linear programming, meta- heuristics).

• Knowledge of AI and machine learning is a plus.

• Experience in developing resolution methods for routing problems of vehicles will be appreciated.

• Good level in English.

Application File: Please send your application in the form of a pdf file (if possible a single file) including a detailed curriculum vitae, transcripts, copies of the most recent master's or engineering diplomas as well as, as far as possible, a cover letter and any recommendations and/or the internship report to the contact emails below:

Contact : • Oumayma BAHRI ([email protected]), LIST3N Lecturer, UTT.

• Lionel Amodeo [email protected]), University Professor, LIST3N, UTT.

Presentation of the establishment and host lab: UTT (University of Technology of Troyes) - Website: http://www.utt.fr

UR LIST3N (IT and Digital Society Laboratory) - Website: https://recherche.utt.fr/list3n

Dear all, We are seeking candidates for a PhD position on "Machine Learning For Fresco Reconstruction".

The detailed offer, instructions to apply, as well as application link can be found on this page:

https://nicolaslerme.fr/my_docs/sujets/these_2021-2022.pdf

Please do not hesitate to diffuse or share the offer!

We are looking for a candidate or a candidate for a thesis topic in Artificial Intelligence / Machine Learning, for an application in Health.

Start date september or october 2022

Subject The thesis work is part of a highly interdisciplinary context (AI/Health). The aim of the thesis is to develop an automated manager of scenarios for the simulation of anesthesia on a digital patient, for the training of interns and nurse anesthetists. These scenarios must be reactive to the actions of the intern or nurse in training. The key to the problem is to know how to simulate the evolution of the physiological parameters of the digital patient according to the action activated by the person in training or by the rest of the medical team (which is virtual). It is also necessary to know how to predict the next action of the medical team (excluding people in training). We can take advantage of the data recorded during the anesthesia of a cohort of real patients, effective for the same surgery, to predict the evolution of the digital patient over the course of the actions performed on the patient. For each patient in the cohort, we also consider the trace of medical actions performed on the patient, in the operating room. These traces are used to predict the next action to be taken, over the course of a scenario. From an AI point of view, we are therefore faced with a problem of semi-supervised learning from multivariate time series and traces of interdependent events, with the aim of predicting short-term and real-time multivariate time series, and predicting the next event.

Description of the subject https://uncloud.univ-nantes.fr/index.php/s/KB9g5ZqdMmspYq3

Frame Christine Sinoquet (web page), Thesis Director, HdR Lecturer in Computer Science at the Laboratoire des Sciences du Numérique de Nantes / UMR CNRS 6004 and Corinne Lejus-Bourdeau, University Professor – Hospital Practitioner, Doctor of Medicine at the Department of Anesthesia-Surgical Resuscitation of the University Hospital of Nantes / Hôtel Dieu – Woman-Child-Adolescent Hospital, Director of the Experimental Laboratory of Simulation of Intensive Medicine of the University of Nantes (LESiMU).

Keywords Artificial intelligence, health of the future, digital patient, digitally assisted training, operating room, anesthesia, simulation, modeling, semi-supervised learning, deep learning, time series, event traces

Profile Master or equivalent in Mathematics or Mathematics/Computer Science or Computer Science with specialization in Data Science or Probability/Statistics, as well as Machine Learning (preferably including deep learning)

Theoretical skills and experience required in probability/statistics, applied mathematics, machine learning

If the candidate has no experience in composition modeling models using deep neural networks, which is one of the axes of investigation of the thesis, he must on the other hand show a strong motivation to invest in this field

- Interest in interdisciplinarity (health) - Experience in programming and good level of programming - Good writing skills - Ability to work in a team, ability to report on the progress of his work

Financing This research will be funded as part of the AIby4 ( AIb y4 ) project. AIby4 is one of the 22 projects selected by the ANR for its call for "doctoral contracts in AI" (2020-25).

A Ph.D. position on "Physics-guided Machine Learning" is open at the University of Saint-Etienne.

The topic is the one of integrating physical knowledge into Machine Learning approaches.

Application deadline: May 22nd, 2022

More information on the subject and the application process:

https://laboratoirehubertcurien.univ-st-etienne.fr/en/teams/data-intelligence/job-opportunities/phd-offer-physics-guided-machine-learning.html

Université Paris-Saclay is recruiting a PhD in computer science to develop partnerships and the valorization of research of the "Graduate School of Computer Science", that is to say the structure that brings together the 22 laboratories in computer science and digital sciences of the university.

https://www.universite-paris-s aclay.fr/offres-emploi/manager-de-projet-h/f-pour-la-graduate-school-gs-informatique-et-sciences-du-numerique-computer-science

You are a doctor in computer science but you do not want to do MCF. You know the academic world well and you have a broad vision of computer science. You want to participate in the construction and development of the university while remaining close to research. You want to work with researchers from all walks of life (universities, schools, ONR...).

Your mission will be to represent our university to partners and you will work directly with research teams to help them develop projects.

A thesis offer on the design of question-and-answer models to solve multi-faceted information needs is to be filled at ISIR - Institute of Intelligent Systems and Robotics (Sorbonne University) and IRIT - Institute for Research in Computer Science of Toulouse.

Titre de la thèse : Response generation models for solving multi-faceted information needs CIFRE thesis in collaboration with Ecovadis France

Background: The prospect of new information retrieval (IR) systems (e.g., research-oriented conversational systems or systems supporting complex research tasks) has fostered the search for theoretical models of information retrieval that take advantage of user interactions or take them into account, for example, through the clarification of questions or interactive models. However, very little work focuses on how to interact with the user in natural language, which is essential, for example for conversational systems.

Project Description: The main objective of the thesis is to design question-and-answer models aimed at solving multi-faceted information needs. In particular, given a collection of documents, our goal is to generate structured and comprehensive responses, covering all facets of a complex information need.

To do this, approaches and models from information retrieval (IR) and natural language processing (NLP) will be used. These two areas of research exploit learning techniques (DL) to model text semantics and generate new knowledge. More specifically, we have shown in a preliminary work [DGS+22] the potential of "data-to-text" approaches [PDL19a, RSSG20, PDL19b] for the generation of complex responses.

Our long-term goal is to adapt to the context of conversational search and take into account user interactions and conversation context [EPBG19, TY20], as well as to include search task-oriented features in the build process [FWZ+20, ZZW+20]. Two main lines of research stand out:

- one is related to the multiplicity of data sources (text, tables, figures, etc.) used to generate the text and output structure. - The other is more related to the user's satisfaction with the output itself.

The generated document must be complete, understandable and explainable.

The application to industrial use cases will be considered in collaboration with the Ecovadis development team. All our models will be evaluated on academic benchmarks, allowing a quantitative evaluation and the publication of the results obtained.

Profile sought: Master's degree or engineering degree in computer science or applied mathematics related to machine learning, natural language processing or information retrieval.

The candidate must have a strong scientific background, good technical programming skills, and must be able to read and write English fluently.

Start and duration (planned): October/November 2022, 36 months

Learn more: https://www.isir.upmc.fr/ contact us/oppotunites/

Fiche de poste : https://www.isir.upmc.fr/wp-content/uploads/2022/04/Response-generation-models-for-solving-multi-faceted-information-needs.pdf

The position offered is a 12-month fixed-term contract, a public law contract under the provisions of the management framework of the Institut Mines Télécom - profession P - post-doctoral student - category II.

Salary: €30,644 gross per year.

3.3. How to apply Applications (CV and cover letter) should be sent exclusively via the link below: https://institutminestelecom.recruitee.com/o/postdoctorante-projet-mask-covid

3.4. Recruitment process Closing date for applications: 14/05/2022

Expected indicative date of the jury: early June

Desired start date: 06/15/2022

3.5.People to contact  On the content of the post: Jacky Montmain (IMT Mines Alès professor), [email protected] , 04.34.24.62.94-06.29.53.15.71

 On the administrative aspects: Géraldine BRUNEL (head of the management service human resources), [email protected], 04.66.78.50.66.