We have a fully funded PhD. sholarship in Artifical Intelligence open at INSA Rouen Normandy, France within the ANR project MultiTrans.
Title: "Improving Autonomous Vehicles perception using Transfer Learning and Vehicle-Infrastructure Cooperation strategies"
Full details (including application instructions): https://anr-multitrans.github. io/jobs/
Application deadline: May 27th 2022.
Expected starting date: Sept. - Oct. 2022.
Research Lab: LITIS Lab, INSA Rouen Normandie, France.
PROMES-CNRS (UPR 8521) proposes three thesis topics (see attached files ) related to artificial intelligence:
1. Development and experimental validation (MicroSol-R) of algorithms for the advanced control-command of solar power plants with cylindrical-parabolic collectors equipped with storage systems.
2. Thermophysical characterization of materials by photothermal method and machine learning - In situ evaluation of the aging of solar receivers.
3. Predictive control of photovoltaic solar power plants equipped with batteries.
Thank you for disseminating the topics. Do not hesitate to contact me (Stéphane Grieu, [email protected]) if you want more information. Best regards.
https://selexini.lis-lab.fr/ jobs/2022/03/29/engineer-position
* Duration: 12 months
* Start: June 2022 (adaptable)
* Application: before May 2, 2022 by email to [email protected]
* Location: LIS http://www.lis-lab.fr , TALEP team https://talep.lis-lab.fr , Aix Marseille University https://www.univ-amu.fr , Luminy campus https://sciences.univ-amu.fr/ sites-geographiques/site-luminy , Marseille
* Remuneration (CDD): €1,600 to €2,000, depending on experience
The objective of the ANR project *SELEXINI https://selexini.lis-lab.fr is to develop original methods of *lexicon induction* automatic language processing The lexicons produced by *clustering* will bring together occurrences of words according to their meanings, but will also contain polylexical expressions, semantic *frames* , argumental structure, generated definitions , etc.
Lexicon induction methods will rely on neural language models (e.g. FlauBERT, CamemBERT) and existing lexical resources (e.g. Wiktionary).
The engineer recruited will be responsible for setting up the initial infrastructure of the project*, both in terms of data and tools. The mission will take place in 5 stages: (1) preparation of a large raw corpus representative of various registers of written French, (2) pre-processing of the corpus using parsers, etc., (3) extraction and structuring of French Wiktionary entries, (4) adaptation (*fine-tuning*) of language models on the project corpus, (5) alignment of words and expressions polylexicals extracted from Wiktionary with occurrences of the corpus.
* Master or thesis in a field related to automatic language processing
* Notions of French and English
* Interest in languages and familiarity with language
technologie
your CV and a few lines explaining why you are applying to carlos.ramisch [AT] lis-lab.fr before May 2, 2022.
4 offers for contracted teacher-researchers in 27th section at La Rochelle University (research at L3i):
https://www.univ-larochelle. fr/wp-content/uploads/pdf/Profil-de-Poste-et-commission-ECC-27-vdrh .pdf
https://www.univ-larochelle. en/wp-content/uploads/pdf/PROFILES-ECC27-IUT-combines-vdrh-1 .pdf (on this document appear 3 job profiles, one after the other)
Please forward them to anyone who may be interested.
The contact person is: Yacine GHAMRI-DOUDANE, Director of L3i ([email protected])
Hello We are looking for 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 anesthesia simulation scenarios 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 triggered 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, operated 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 have the trace of the medical actions carried out 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.
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 (AIby4) project. AIby4 is one of the 22 projects selected by the ANR for its call for "doctoral contracts in AI" (2020-25).
Selection Calendar Sending the application file, contacts and audition (if the profile of the candidate allows it) to be carried out before the strict deadline of Thursday, May 12, 2022.
Decision: Mid-May 2022
Documents to be provided - Detailed CV - cover letter - Master 1 transcript (with ranking rank and promotion staff)
- Master 2 grades excluding internship (with ranking rank and promotion staff) - summary e of the current internship (between 2 and 4 pages, bibliographic references extra)
- letters of recommendation for the current year - contact details of referees (first name, last name, status, institution (detail the acronyms if necessary), city, email address, telephone number)
Questions and sending of application files (zip archive) to [email protected]
Dear all, We are welcoming candidates with expertise in the areas of Computer Vision and Avionics for a two year Innovate UK funded project.
Details are about how to apply are given below:
Research Assistant/Associate in Computer Vision for Enhancing Autonomy The University of Sheffield Dept. of Automatic Control and Systems Engineering
Closing Date: 19th May 2022
https://www.jobs.ac.uk/job/CPD144/research-assistant-associate-in-computer-vision-for-enhancing-autonomy-of-swarms-of-uavs?utm_campaign=google_jobs_apply&utm_source=google_jobs_apply&utm_medium=organic
The project is an exciting opportunity to enhance the autonomy of UAVs, to work both with academic and industrial partners.
english announcement at https://anr-grifin.telecom-sud paris.eu/post/2022-04-26-phd-position-offer/ ) as part of the ANR GRIFIN project, we are recruiting a PhD student to work in the field of next-generation network security (IoT-based use cases) and in particular on the intelligent and automated selection and deployment of countermeasures, and verification of their implementation:
https://www.adum.fr/as/ed/voir proposal.pl?site=adumR&matr icule_prop=43017
The thesis is co-supervised by Télécom SudParis, LORIA and LIP6.
The application deadline is June 30, 2022. The requested documents are as follows: - a CV (up to date) - a letter of motivation
- a copy of the Master's or engineering degree (if available) as well as M1/M2 statements - letters of recommendation (training manager, researcher, internship supervisor, etc.) - a copy of an identity document (if you do not have French nationality)
We are looking for a candidate for a PhD thesis in computer science, the subject of which is “Human/Machine collaboration for complex tasks realization”.
Host laboratory: Connaissance et Intelligence Artificielle Distribuées (CIAD) – http://www.ciad-lab.fr Belfort, FRANCE
A detailed description of the subject is in the attached PDF, and also available online using this link:
https://www.utbm.fr/wp-content/uploads/2022/04/CIAD-1.pdf
The application deadline is May 20.
The Cybersecurity and Digital Sovereignty" chair at IHEDN is offering a PhD on the topic: Reconfigurable and highly interactive deception techniques for industrial control systems .
The detailed description is available at the following link: https://www.adum.fr/as/ed/voirproposition.pl?site=adumR&matricule_prop=43084
- Starting date : now - The thesis will be supervised by Jean Leneutre (LTCI, ACES team, https://www.telecom-paris.fr/en/research/laboratories/information-processing-and-communication-laboratory-ltci/research-teams/autonomous-critical-embedded-systems) and will have an industrial tutor, a research engineer at Naval Group.
- The thesis will be mainly co-located at the Institut des Hautes Études de Défense Nationale (IHEDN) located at École Militaire in Paris, and at Télécom Paris in Palaiseau. Occasionally, short stays on the Naval Group site in Ollioules (Toulon) will be organized. - The PhD student will be registered at the doctoral school of the Institut Polytechnique de Paris (IP Paris). - Gross salary will be 2200 euros.
Candidates should send an electronic file to [email protected] containing the following documents : - a motivation letter, - a detailed CV,
- post-baccalaureate transcripts (at least M1 and M2 or equivalent) indicating, as far as possible, the ranking in the class, - one or two recommendation letters,
- a document (internship report, study project report, article ...) written in English (or French) describing one of your contributions in the field of computer science and networks.
Jean LENEUTRE, Associate Professor, Institut Polytechnique de Paris, Telecom Paris, LTCI Lab (Information Processing and Communication Laboratory), Computer Science & Networking Dpt, ACES (Autonomous and Critical Embedded Systems) team, 19 Place Marguerite Perey, 91120 Palaiseau, FRANCE Office : 4D46 Tel. : +33 (0)1 75 31 97 78
We are looking for a candidate for a thesis in virtual reality and robotics
for an application on the semi-autonomous vehicle (start of thesis in October 2022).
Thank you for circulating this offer.
Sincerely,
Indira Thouvenin https://www.hds.utc.fr/~ithouven/dokuwiki/en/start HDR Teacher-Researcher - UMR CNRS 7253 Heudiasyc
Sorbonne Universities, University of Technology of Compiègne
Computer Engineering - office 141 57 avenue de Landshut 60203 Compiègne (France)
Indira Thouvenin /[email protected] / +33 344234547 Reine Talj/ [email protected]/ +33 3 44 23 46 31
Developing systems towards robust discourse parsing and its application
- Contract duration: 12 months
- Starting date: May 2022 (flexible)
- Location: IRIT, P. Sabatier University (Toulouse III)
- Remuneration: TODO 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://www.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, discourse 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, e.g. [1,2,3]. However, current performance are still low, mainly due to the lack of annotated data (see e.g. [4] on monologues, [5] on dialogues, [6,7] for the multilingual setting).
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 recruited candidate will work on one or several of the following topics, depending on its interests:
- Data representation: Discourse processing requires information from various levels of linguistics analysis. For now, existing studies do not make it clear what kind of information is important and needed, and some potentially relevant sources of information are ignored. We plan to explore this issue within a multi-task learning setting, where a system has to jointly learn different tasks. We will experiment on classification tasks (discourse relation, segmentation) and on full discourse parsing.
- Transferring to new languages, domains and modalities: Developing systems that perform well on domains or languages (that are) different from those used at training time is crucial, especially if the adaptation can be done in an unsupervised way. It is especially important for discourse, since annotation is very hard and time-consuming. We plan to experiment with cross-lingual embeddings and to explore multi-task learning, but trying to understand how to integrate additional linguistic information with only little annotated data for auxiliary tasks. We also want to investigate dialogues, for which only a few discourse parsers exist, and better understand how it differs for monologues.
- Extrinsic evaluation: We will investigate a few downstream applications that could benefit from discourse information, as a way to give an extrinsic evaluation. We will explore pipeline systems, varying the way we encode the discourse information as input of our end system. We will also explore transfer learning strategies, either via multi-task learning or representation learning. We plan to start with cognitive impairment detection (e.g. schizophrenia, Alzheimer) and argument mining. More applications will be considered, depending on the interest of the recruited postdoc.
It will be possible to investigate other paths of research, such as few-shot or unsupervised learning, depending on the interest of the recruited candidate.
The position is funded by the ANR AnDiAMO project, for which an engineer 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.
Profile
- PhD degree in computer science or computational linguistics - Good knowledge in Machine Learning - Interest in language technology / NLP - Good programming skills: preferably with Python, knowledge of PyTorch is a plus
Application Please send a CV and a few lines explaining your interest for the position to [email protected]
ISEN Ouest is actively developing and is currently recruiting new teachers and researchers in computer science/ geomatics on permanent contracts for its Nantes site.
ISEN offers its students different Post-BACCALAUREATE courses. Among these courses, the "Environment Science and Technology" (EST) cycle is aimed at students interested in the environment, sustainable development and technologies. This cycle is already in place in Brest more recently in Nantes. The main mission of the recruited teacher-researcher will be to support the development of the EST cycle in Nantes.
The teacher-researcher will join the "Knowledge Learning and Information Modeling" (KLaIM) research team at the LabISEN Laboratory.
The research activities carried out by the teacher-researcher must interface with the themes of the KLaIM team with in order of preferences according to the skills already existing on the Nantes site:
Multi-agent simulations and multi-criteria decision making Graph mining algorithms and models Mining and extraction of knowledge in masses of spatialized data (pattern mining) We are looking for a PhD with training/experience in geomatics and computer skills.
A profile with experience in business, setting up industrial projects and/or having supervised research activities (doctoral students, post-doctoral fellows) would also be appreciated.
The position is to be filled on a contract of indefinite duration under framework status to the fixed-day package.
It is located in CARQUEFOU. The salary is to be agreed according to experience.
Recruitment procedure: The position on a permanent contract is to be filled no earlier than 1 June 2022 and no later than 1 September 2022.
Candidates must provide a cover letter, a curriculum vitae (2 pages maximum). A list of publications mentioning those in progress and/or to come.
Applications will be reviewed over time.
The detailed job descriptions as well as the application procedure can be consulted on our website at the following address:
https://isen-brest.fr/jobs/
1 - Context and funding Constraint-based pattern mining is a fundamental data mining task, extracting locally interesting patterns to be either interpreted directly by domain experts, or to be used as descriptors in downstream tasks, such as classification or clustering.
Recently, this approach has been challenged by an increasing focus on user-centered, interactive, and anytime pattern mining [2].
This new paradigm stresses that users should be presented quickly with patterns likely to be interesting to them, and typically affect later iterations of the mining process by giving feedback. A powerful framework for taking a variety of user feedback into account is pattern mining via constraint programming (CP). Much of the current focus in this domain is on user- centered/interactive mining, particularly the ability to elicit and exploit user feedback [1,2].
An important aspect of requesting such feedback is that the user be quickly presented with diverse results. If patterns are too similar to each other, deciding which one to prefer can become challenging, and if they appear in several successive iterations, it eventually becomes a slog. Similarly, a method that produces diverse results but takes a long time to do so, risks that the user checks out of the process. Sampling algorithms [3,4] can circumvent these negative complexity results by sampling a representative set of patterns, according to a probability distribution that is proportional to a given quality measure, without explicitly enumerating all patterns. These approaches can substantially improve efficiency as well as controllability of pattern discovery processes.
While a number of pattern sampling approaches have been developed over the past years, they are either inflexible (as they only support a limited number of quality measures and constraints), or do not provide theoretical guarantees concerning the sampling accuracy. At the algorithmic lever, they mainly rely on the Markov Chain Monte Carlo random walks over the pattern space [3,4], or a special purpose sampling procedure tailored for a restricted set of itemset mining tasks [8].
Other approaches use recent advances in sampling solutions in SAT to partition the search space into cells using random XOR constraints and then extracts a pattern from a randomly selected cell [5]. This solution space reduction approach has also been transposed to constraint programming [6,7].
In the last decade, data mining has been combined with constraint programming to model various data mining problems [9,10,11]. The main advantage of CP for pattern mining is its declarativity and flexibility, which include the ability to incorporate new user-specified constraints without the need to modify the underlying system. Similarly, some recent works in CP for counting solutions of individual constraints have been proposed [12,13,14]; in particular Truchet and Pesant collaborated on this subject [12]. All these methods to count or to sample rely on close mathematical techniques or models, as has been shown in the case of SAT problem [15].
Research project The focus of this thesis is to develop new methods for sampling and counting for interactive, and anytime pattern discovery. The methods will be studied through the prism of new class of constraints, pattern constraints, dedicated to model some complex tasks in data mining.
These constraints, which are based on new structures, remain a scientific challenge. Thanks to the flexibility of the CP framework, a variety of pattern quality measures will be considered to sample patterns while still providing strong theoretical guarantees.
Team supervision and PhD registration The university partner Polytechnique Montréal, and more specifically the Quosséça research center, is a major actor in AI in Canada and at the international. It is involved in a large number of industrial and academic projects.
Supervisors: Gilles Pesant, laboratoire Quosséça, Polytechnique Montréal, [email protected] Samir Loudni, IMT Atlantique, LS2N, [email protected] Charlotte Truchet, Nantes University, LS2N, [email protected]
Gilles Pesant and Charlote Truchet have recently collaborated as part of Giovanni Lo Bianco's thesis, on the enumeration of solutions for the global cardinality constraint.
This thesis will extend this work to the context of interactive pattern discovery. Samir Loudni brings his expertise on the triptych (A) constraints, (B) symbolic data mining, (C) preferences.
The student will divide its time into two periods, one in Canada and one in France, where frequent working visits and collaborations will take place from one institution to the other.
Candidate profile The successful candidate will have (or will soon obtain) an MSc (or similar) in Computer Science or related subject.
Required skills:
Constraint Programming, mathematics, machine learning.
Strong facility in software engineering and implementation (Java, Python)
Strong mathematical and formal foundations
A good command of written and oral English
How to apply Send a leler of motivation, transcript of grades, and your CV with the subject beginning with [AI PhD] to
Pr Samir Loudni ([email protected])
Pr Gilles Pesant ( [email protected] )
Dr Charlotte Truchet ( [email protected] )