Offre en lien avec l’Action/le Réseau : RoCED/– — –

Laboratoire/Entreprise : LAMSADE – PSL – Université Paris Dauphine
Durée : 3 ans
Contact : kbelhajj@googlemail.com
Date limite de publication : 2023-04-15

Contexte :
The crucial role of data protection in safeguarding personal information, preventing malicious use of data, and ensuring responsible handling of data by organizations is now widely acknowledged. We have an opening on this subject 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

Sujet :
The PhD Student will be investigating techniques for profiling and linking datasets that would help data providers to gain insight into their data and to choose data protection strategies that go beyond privacy protection to take into account the protection of the data provider’s economic assets. We aim to provide an end-to-end solution that helps data providers understand their data, identify the links that connect them as well as the links that connect them to external datasets, identify links that could be used maliciously to obtain privacy-intrusive information, and protect their datasets accordingly before sharing. The problems investigated and solutions developed will be guided and validated within case studies in the fields of health and economics.

Profil du candidat :
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). PSL is the first French university in Times Higher Education ranking.

Formation et compétences requises :
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.

Adresse d’emploi :
Interested candidates are invited to send the following to khalid.belhajjame@dauphine.fr 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

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : ICube (Université de Strasbourg / CNRS)
Durée : 36 mois
Contact : fabrice.theoleyre@cnrs.fr
Date limite de publication : 2023-03-31

Contexte :
== Environment ==

Founded in the 16th century, the University of Strasbourg has a long history of excellence in higher education, rooted in Renaissance humanism. The University of Strasbourg is a public research university located in Strasbourg, France, with over 52,000 students. With around 650 members, the ICube lab is a major driving force for research in Strasbourg whose main areas of application are computer science, robotics, and engineering science.

== Equal Employment Opportunity ==

We aim to increase the proportion of women and therefore qualified female applicants are explicitly encouraged to apply. We prohibit any type of discrimination (gender, ethnicity, religion or ideology, age, sexual orientation). ICube has set-up anti-discrimination and anti-harassment committees.

Sujet :
Several PhD positions require strong skills in Machine Learning, and Data Science for experimental analysis:

– Cybersecurity through Digital Twins for the Industrial Internet of Things, under the supervision of Fabrice Théoleyre (CNRS)

– Energy-efficient routing in multi-technology low-power and lossy wireless networks, under the supervision of Julien Montavont and Pascal Mérindol

– Inter-domain monitoring and predictions for service orchestration in 5G, under the supervision of Fabrice Théoleyre (CNRS) and Julien Montavont

– Software-defined low-power and lossy wireless networks, under the supervision of Thomas Noël and Julien Montavont

– AI-Powered Reliable and Available Wireless Mesh Networks for the Factory of the Future, under the supervision of Fabrice Theoleyre (CNRS), Quentin Lampin, and Marion Dumay (Orange Labs).

– Better Algorithms for Secure and Efficient Blockchains, under the supervision of Quentin Bramas

A detailed description for each position is available online at https://reseaux.icube.unistra.fr/en/index.php/Job_opportunities

Profil du candidat :
Computer Scientist with the willingness to develop his/her skills in networking.

Applications will be closed on January 20, 2023.

Formation et compétences requises :
— Excellent programming skills in C, and embedding programming;
— Distributed algorithms;
— Wireless networks (protocols and radio propagation), energy efficiency;
— Applicants should possess good verbal and written English skills. French is not a requirement;
— Holding an MSc in Computer Science (CS) or Electrical and Computer Engineering (ECE), or Electrical and
Computer Engineering (ECE) is mandatory

Adresse d’emploi :
Illkirch-Graffenstaden (Starsbourg)

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : Université de Caen Normandie / laboratoire GREYC U
Durée : 3 ans
Contact : olivier.lezoray@unicaen.fr
Date limite de publication : 2023-03-31

Contexte :
Offre de thèse en Informatique à l’Université de Caen Normandie / laboratoire GREYC UMR CNRS 6072

Titre : Apprentissage profond d’arbres binaires de partitions pour l’analyse d’images

Mots-Clés : Représentation hiérarchiques, Arbres binaires de partition, Apprentissage Profond, Ultramétriques.

Sujet :
Sujet
—–
Il existe de nombreuses représentations des images numériques, chacune adaptée à différents contextes. Dans cette thèse nous nous intéressons aux représentations hiérarchiques des images. Ces dernières permettent, à partir d’une sur-segmentation d’une image en super-pixels, de procéder à des fusions de régions à différentes échelles. De telles représentations hiérarchiques permettent donc de capturer les caractéristiques des images à différentes échelles simultanément, et sont facilement interprétables et manipulables par un humain. Construire des représentations hiérarchiques de bonne qualité est alors une étape très importante de l’analyse des images. En analyse d’images, les arbres binaires de partitions (ABP) sont une représentation hiérarchique populaire. Leur construction repose sur plusieurs éléments clés: une partition initiale, un modèle de région, un critère de fusion, un ordre de fusion. Cette construction de l’ABP repose alors souvent sur des descripteurs de régions peu adaptés aux données et sur des méthodes heuristiques et gloutonnes de clustering hiérarchique. Nous proposons de tirer parti de l’apprentissage profond pour la construction et la manipulation d’ABPs. La construction de l’arbre pourra alors exploiter des descripteurs profond de super-pixels, apprendre la similarité entre ces descripteurs et enfin disposer d’un critère de fusion appris. Une ultramétrique étant une représentation duale d’une représentation hiérarchique, des méthodes d’apprentissage profond peuvent être envisagées pour apprendre non pas l’ABP mais directement l’ultramétrique à partir d’un graphe représentant la sur-segmentation et en minimisant explicitement une fonction de coût. La segmentation sémantique d’une image pourra être ensuite vue comme soit une labelisation apprise des sommets de l’ABP, soit l’apprentissage d’une coupe dans l’ABP. Un arbre étant un graphe, des réseaux de neurones à convolution sur graphes pourront être envisagés pour cela (la convolution et le pooling étant là très particuliers étant donné la structure d’arbre du graphe). Enfin, Des applications en santé (mélanome de la peau) et en imagerie satellitaire seront effectuées.

Profil du candidat :
Les candidats doivent être titulaires d’un master ou d’un diplôme d’ingénieur dans un domaine lié à l’informatique ou aux mathématiques appliquées, et posséder de solides compétences en programmation (en particulier avec des cadres d’apprentissage profond). Une expérience dans le domaine du traitement des images sera un atout. Les candidats doivent être capables de rédiger des rapports scientifiques et de communiquer les résultats de leurs recherches lors de conférences en anglais.

Formation et compétences requises :

Adresse d’emploi :
Caen

Document attaché : 202302151546_sujetTheseLezoray2023_fr.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Mines Saint-Etienne
Durée : 3 ans
Contact : victor.charpenay@fau.de
Date limite de publication : 2023-03-31

Contexte :
The thesis is equally funding by ANR (Agence Nationale de la Recherche) and elm.leblanc, one of the leading home automation system vendors. One of the main technical challenges in modern home automation is to using Artificial Intelligence (AI) to minimize the energy consumption of technical systems without loss of comfort. For instance, the production of hot water can be optimized by dynamically adapting the temperature of water and the time of use of the boiler based on activities monitored in the home. The general objective of the thesis is to monitor human activities without ubiquitous sensing capabilities.

Sujet :
The domain of research of the thesis is knowledge representation and reasoning, a subfield of AI. Its objective is to evaluate abductive reasoning methods over sensor measurements performed in a home environment. Abductive reasoning in this context consists in finding logically sound hypotheses (e.g. ‘the dishwasher is on’) that explain observed sensor measurements (‘electric consumption has risen in the last two hours’) according to a model of human activity in a home.

The baseline assumption of the thesis is that only minimal sensing is available in the home, as is the case in most homes today: smart meters provide aggregated values (every hour/day) but no information is available per room. Abductive reasoning is expected to help optimize home automation systems without relying on some ubiquitous sensing apparatus (which raises environmental, technical and privacy-preservation questions).

Several abduction mechanisms will be evaluated, including Abductive Logic Programming (for an exhaustive exploration of hypothesis space) and neural-symbolic integration methods (for a probabilistic exploration of hypothesis space).

Profil du candidat :
Prior knowledge in AI is expected, either in neural networks or in computational logics, logic programming and/or Semantic Web technologies. Basic understanding of statistical inference methods and linear programming is also considered important. Technical skills required for the thesis include: multi-paradigm programming (Java, Lisp, R, Prolog, …), data modeling (UML, OWL, E/R, BPMN, …), Linux system administration (Bash, SSH, Docker, …).

Autonomy and curiosity are important soft skills to compete a PhD thesis.

Formation et compétences requises :
Holder of a Master’s degree in computer science or data science. Prior knowledge in AI is expected, either in neural networks or in computational logics, logic programming and/or Semantic Web technologies. Basic understanding of statistical inference methods and linear programming is also considered important. Technical skills required for the thesis include: multi-paradigm programming (Java, Lisp, R, Prolog, …), data modeling (UML, OWL, E/R, BPMN, …), Linux system administration (Bash, SSH, Docker, …).

Adresse d’emploi :
Espace Fauriel, Saint-Etienne

Document attaché : 202302151304_phd-offer.pdf

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : ICube – Université de Strasbourg
Durée : 3 ans
Contact : lampert@unistra.fr
Date limite de publication : 2023-03-31

Contexte :
L’équipe SDC du laboratoire ICube (Université de Strasbourg) en collaboration avec le CNES propose un contrat doctoral sur l’apprentissage de représentation enanalyse de séries temporelles d’images de télédétection.

https://recrutement.cnes.fr/fr/annonce/2035525-23-111-representation-learning-for-geographic-spatiotemporal-generalisation-67400-illkirch-graffenstaden

La date limite de candidature est fixée au 16 mars et doit se faire via le site du CNES (lien donné ci-dessus).

Si vous êtes intéressé, veuillez prendre contact avec nous le plus rapidement possible en envoyant un mail (joindre votre CV, lettre de motivation et relevés de notes, avec si possible vos classements en L3, M1 et éventuellement M2, … ) à lampert@unistra.fr et gancarski@unistra.fr

Sujet :
Titre du thèse : Representation Learning for Geographic Spatio-Temporal Generalisation

Description du sujet : Time-series are becoming prevalent in many fields, particularly when monitoring environmental changes of the Earth’s surface in the long term (climate change, urbanisation, etc), medium term (annual crop cycle, etc) or short term (earthquakes, floods, etc). With the current and future satellite constellations satellite image time-series (SITS) expand remote sensing’s impact. The project’s goal is to develop domain invariant representations using deep learning for SITS analysis. Such methods will enable geographic generalisation, which consists of reusing information from the analysis of one geographic area to analyse others by using, or not, the same sensors, as proposed in [5]. Current approaches work for single images because they generally originate from the computer vision community. The internship will start the evaluation of the state-of-the-art and to implement and extend approaches already developed in ICube [5,6]. Current work on domain adaptation (DA) for time-series uses either weak supervision [1] or attention-based mechanisms [2,3] for classification or focus on the related problem of time-series forecasting [4]. However, none of these approaches tackle the problem of learning DIRs that can be applied to several geographical locations simultaneously. The work has two benefits: on the one hand, to reduce the burden of ground truth collection when sensors of different characteristics are used; and on the other to exploit the information contained in each data modality to learn representations that are more robust and general, i.e. to detect crops, land cover evolution, etc in different countries that exhibit different characteristics. Your contributions will be part of the global work of the SDC researchers and will be validated through the partnership with CNES and potential collaboration with Tour du Valat. SDC’s aim is to propose and implement new generic methods and tools to exploit large sets of reference data from one domain/modality (sufficient to train an accurate detector) to train a multi-modal/domain detector that can be applied to imagery taken from another sensor for which there exists no reference data. As such, the work tackles key problems in many machine learning & computer vision applications.

Profil du candidat :
Master en Informatique ou équivalent.

Formation et compétences requises :
Compétences fortes en machine learning et analyse d’images. Une expérience en apprentissage profond est un plus indéniable.

Adresse d’emploi :
ICube
Université de Strasbourg