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

Laboratoire/Entreprise : LIMOS / Mines Saint-Étienne
Durée : 3 ans
Contact : victor.charpenay@emse.fr
Date limite de publication : 2022-07-31

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. 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 :
Candidates are expected to have prior knowledge in AI, especially in computational logics, logic programming and/or Semantic Web technologies. Basic understanding of statistical inference methods and linear programming is also considered relevant.

Candidates whose background is machine learning may apply as well. A cover letter exposing the candidate’s motivation to combine (neural) learning methods with symbolic AI is however expected.

Formation et compétences requises :
Holder of a Master’s degree in computer science or data science. 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 :
Saint-Étienne (with stays in Paris and/or Lille-Douai)

Document attaché : 202206071402_phd-offer.pdf

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

Laboratoire/Entreprise : Inria Nancy & Lille
Durée : 36 mois
Contact : emmanuel.vincent@inria.fr
Date limite de publication : 2022-07-31

Contexte :
This PhD is part of the “Personal data protection” project of PEPR Cybersécurité, which aims to advance privacy preservation technology for various application sectors. It will be co-supervised by Emmanuel Vincent and Marc Tommasi. The PhD student will have the opportunity to spend time in both the Multispeech and Magnet teams, to collaborate with 9 other research teams in France and with the French data protection authority CNIL, and to contribute to the project’s overall goals including the organization of an anonymization challenge.

Sujet :
Large-scale collection, storage, and processing of speech data poses severe privacy threats [1]. Indeed, speech encapsulates a wealth of personal data (e.g., age and gender, ethnic origin, personality traits, health and socio-economic status, etc.) which can be linked to the speaker’s identity via metadata or via automatic speaker recognition. Speech data may also be used for voice spoofing using voice cloning software. With firm backing by privacy legislations such as the European general data protection regulation (GDPR), several initiatives are emerging to develop and evaluate privacy preservation solutions for speech technology. These include voice anonymization methods [2] which aim to conceal the speaker’s voice identity without degrading the utility for downstream tasks, and speaker re-identification attacks [3] which aim to assess the resulting privacy guarantees, e.g., in the scope of the VoicePrivacy challenge series [4].

The first objective of this PhD is to improve the privacy-utility tradeoff by better disentangling speaker identity from other attributes, and better decorrelating the underlying dimensions. Solutions may rely on suitable generative or self-supervised models [5, 6] or on adversarial learning [7]. The resulting privacy guarantees will be evaluated via stronger attackers, e.g., taking metadata into account.

The second objective is to extend the proposed audio-only approach to multimodal speech (audio, facial video, and gestures). Solutions will exploit existing facial anonymization technology [8]. A key difficulty will be to preserve the correlations between modalities, which are essential for training multimodal voice processing systems.

Depending on the PhD student’s skills, additional directions may also be explored, e.g., evaluating the proposed anonymization solutions in the context of federated learning.

[1] A. Nautsch, A. Jimenez, A. Treiber, J. Kolberg, C. Jasserand, E. Kindt, H. Delgado, M. Todisco, M. A. Hmani, M. A. Mtibaa, A. Abdelraheem, A. Abad, F. Teixeira, M. Gomez-Barrero, D. Petrovska, N. Chollet, G. Evans, T. Schneider, J.-F. Bonastre, B. Raj, I. Trancoso, and C. Busch, “Preserving privacy in speaker and speech characterisation,” Computer Speech and Language, vol. 58, pp. 441–480, 2019.

[2] B. M. L. Srivastava, M. Maouche, M. Sahidullah, E. Vincent, A. Bellet, M. Tommasi, N. Tomashenko, X. Wang, and J. Yamagishi, “Privacy and utility of x-vector based speaker anonymization,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, to appear.

[3] B. M. L. Srivastava, N. Vauquier, M. Sahidullah, A. Bellet, M. Tommasi, and E. Vincent, “Evaluating voice conversion-based privacy protection against informed attackers,” in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2802–2806, 2020.

[4] N. Tomashenko, X. Wang, E. Vincent, J. Patino, B. M. L. Srivastava, P.-G. Noé, A. Nautsch, N. Evans, J. Yamagishi, B. O’Brien, A. Chanclu, J.-F. Bonastre, M. Todisco, and M. Maouche, “The VoicePrivacy 2020 Challenge: Results and findings,” Computer Speech and Language, vol. 74, pp. 101362, 2022.

[5] L. Girin, S. Leglaive, X. Bie, J. Diard, T. Hueber, and X. Alameda-Pineda, “Dynamical variational autoencoders: A comprehensive review,” Now Foundations and Trends, 2021.

[6] A. Baevski, H. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” in Advances in Neural Information Processing Systems, pp. 12449–12460, 2020.

[7] B. M. L. Srivastava, A. Bellet, M. Tommasi, and E. Vincent, “Privacy-preserving adversarial representation learning in ASR: Reality or illusion?” in Interspeech, pp. 3700–3704, 2019.

[8] T. Ma, D. Li, W. Wang, and J. Dong, “CFA-Net: Controllable face anonymization network with identity representation manipulation,” arXiv preprint arXiv:2105.11137, 2021.

Profil du candidat :
Strong programming skills in Python/Pytorch.
Prior experience in speech and video processing will be an asset.

Formation et compétences requises :
MSc in computer science, machine learning, or signal processing.

Adresse d’emploi :
https://jobs.inria.fr/public/classic/en/offres/2022-05013

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

Laboratoire/Entreprise : LIAS, ISAE-ENSMA
Durée : 3 ans
Contact : brice.chardin@ensma.fr
Date limite de publication : 2022-06-24

Contexte :
SRD est un gestionnaire de réseau de distribution d’électricité chargé de gérer, exploiter, entretenir et développer un réseau électrique couvrant 90% de la Vienne. Pour l’optimisation de son réseau et la planification d’investissements, SRD cherche à modéliser le comportement des consommateurs et producteurs qu’il dessert.
Bien que cette modélisation soit principalement basée sur les valeurs historiques de puissance transitant sur le réseau, SRD s’intéresse plus particulièrement à son pouvoir prédictif, c’est-à-dire sa capacité à capturer le comportement futur des éléments considérés.

Sujet :
L’objectif scientifique principal de cette thèse est d’élaborer des techniques de classification permettant d’identifier des groupes d’éléments avec une garantie de dissimilarité maximale entre deux éléments d’un même groupe, et de positionner ce type d’approche par rapport aux algorithmes de partitionnement existants.
Les techniques considérées ici sont basées sur un partitionnement sous contrainte, et plus spécifiquement sous contrainte de dissmilarité intra-cluster maximale. Ce type de partitionnement garantit une certaine proximité entre les membres d’un groupe et leur représentant.

Profil du candidat :
Le candidat devra posséder des connaissances en développement logiciel, systèmes d’information, statistiques et analyse de données.
Un bon niveau en français et en anglais est également nécessaire.

Formation et compétences requises :
Le candidat devra être titulaire d’un master en informatique ou d’un diplôme d’ingénieur.

Adresse d’emploi :
ISAE-ENSMA, 1 avenue Clément Ader, 86360 Chasseneuil-du-Poitou

Document attaché : 202206070936_these_labcom_alienor.pdf

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

Laboratoire/Entreprise : LAMSADE, Université Paris Dauphine
Durée : 3 ans
Contact : kbelhajj@googlemail.com
Date limite de publication : 2022-07-20

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

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

Sujet :
We have an opening for a PhD position with the objective to develop new solutions to help data providers who wish to share their data to better understand it, and to choose the best-suited data protection policies. To do so, the PhD Student will be investigating techniques for profiling and linking datasets that would help data providers to gain insight into their data, to estimate its (economic) value, and to choose data protection strategies that go beyond privacy protection to take into account the protection of the data provider’s economic assets.

Profil du candidat :
We seek strongly motivated candidates prepared to dedicate to high quality research. The candidate should have (or be close to obtaining) a Master’s degree or equivalent in computer science or applied mathematics. Starting date September/2022.

The successful candidate will enroll as a PhD student in the Computer Science department of the Paris-Dauphine University (under the co-direction of myself and Prof. 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).

Formation et compétences requises :
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

Adresse d’emploi :
Université Paris Dauphine, Paris

khalid.belhajjame@dauphine.fr
daniela.grigori@lamsade.dauphine.fr

Document attaché : 202206040950_annoce_phd_position.txt

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

Laboratoire/Entreprise : Vasa (http://vasa.fr)
Durée : 3 ans
Contact : Jean-sebastien.lefevre@vasa.fr
Date limite de publication : 2022-07-20

Contexte :
Dans le cadre de vos recherches, vous aurez à définir une méthodologie permettant de rendre accessible à la compréhension le contenu des graphiques, tableaux et descriptions de chacun les documents de protection sociale via des algorithmes, puis à la déployer, et ensuite à valider la pertinence de cette méthodologie par une stratégie continue d’A/B testing.

Sujet :
Analyse de documents numérisés ou nativement numériques (pdf) composites (textes, graphiques, tableaux) pour l’extraction d’informations complexes pertinentes.

Profil du candidat :
Vous disposez donc d’un niveau bac +5 en Mathématiques Appliqués, en Traitement et Analyse de données, Machine Learning ou similaire
Vous avez un intérêt marqué pour la digitalisation de l’économie et aux nouvelles technologies.

Envie de développer ses connaissances et compétences en :

• Natural Language Processing (NLP) ;
• Heterogenous Data ;
• Image Analysis ;
• Deep Neural Networks for Document Analysis.

Vous parlez couramment français et anglais. Vous savez vulgariser des notions complexes.

Vous travaillerez avec l’équipe de développeurs et de chercheurs pour transformer vos sujets de recherches en solutions commercialisables.

Formation et compétences requises :
bac +5 en Mathématiques Appliqués, en Traitement et Analyse de données, Machine Learning ou similaire

anglais et français courant

Adresse d’emploi :
PINEY (10)

Document attaché : 202206031624_Offre Thèse CIFRE IA V3.docx

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

Laboratoire/Entreprise : Laboratoire des signaux et systèmes (L2S), Univers
Durée : 3 ans
Contact : stefano.fortunati@centralesupelec.fr
Date limite de publication : 2022-09-04

Contexte :
Reinforcement Learning (RL) methodologies are currently adopted in different context requiring sequential decision-making tasks under uncertainty [1]. The RL paradigm is based on the perception-action cycle, characterized by the presence of an agent that senses and explores the unknown environment, tracks the evolution of the system state and intelligently adapts its behaviour in order to fulfil a specific mission. This is accomplished through a sequence of actions aiming at optimizing a pre-assigned performance metric (reward). There are countless applications that can benefit from this perception-action cycle (traffic signal control, robots interactions the physical objects, just to cite a few), each of which is characterized by a peculiar definition of “uncertainty” or “unknown environment”. A more precise definition of this uncertainty strongly depends on the particular domain considered. However, there is at least one crucial assumption underlying the majority of classical RL algorithms: the stationarity of the environment, i.e. the statistical and physical characterization of the scenario, is assumed to be time-invariant. This is clearly a quite restrictive limitation in many real-world RL applications, where the agent is usually embedded in a changing scenario whose both statistical and physical characterization may evolve over time. Due to the crucial importance of including the non-stationarity in the RL framework, both theoretical and application-oriented non-stationary approaches have been proposed recently in the RL literature (e.g. [2], [3]). Among the numerous potential applications, in this project we will focus on the problem of Cognitive Radar (CR) detection in unknown and non-stationary environment. Specifically, building upon the previous works [4], [5], we will aim at proposing an RL based algorithm for cognitive multi-target detection in the presence of unknown, non-stationary disturbance statistics. The radar acts as an agent that continuously senses the unknown environment (i.e., targets and disturbance) and consequently optimizes transmitted waveforms in order to maximize the probability of detection (PD) by focusing the energy in specific range-angle cells.

Sujet :
The scientific goal of the proposed PhD thesis is twofold. Firstly, the PhD candidate will get familiar and develop original RL-based algorithms for non-stationary environments. These theoretical outcomes will be then applied to a specific scenario of great interest nowadays: the radar detection of drones. More specifically, the PhD thesis will be structured in two steps:
1. Theoretical foundation of non-stationary RL algorithms: The aim of this first step is to develop an original theoretical foundation of non-stationary Markov Decision Processes (MDP) [2]. In particular, the candidate will investigate the possibility to generalize classical RL methodologies to MDP characterized by a time-varying sets of states, actions and reward functions. This non-stationary generalization is of crucial importance for a wide variety of applications and it is an almost unexplored research field.
2. Surveillance applications and drone detection: The theoretical results obtained in the first part of the PhD thesis will then be used to derive and implement new algorithms for drones detection and tracking using radar systems [4], [5]. Specifically, we will consider a co-located Multiple-Input-Multiple-Output (MIMO) radar with a large (“massive”) number of transmitters and receivers. It has been shown, in fact, that this massive MIMO configuration allows one to dispense with unrealistic assumptions about the a-priori knowledge of the statistical model of the disturbance [4].

[1] R. S. Sutton, A. G. Barto (2018). Reinforcement Learning: An Introduction. MIT press, Cambridge,
[2] E. Lecarpentier, E. Rachelson, “Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning,” Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019, pp. 7214–7223.
[3] S. Padakandla, K. J. Prabuchandran, S. Bhatnagar, “Reinforcement learning algorithm for non-stationary environments,” Applied Intelligence 50, 3590–3606 (2020).
[4] S. Fortunati, L. Sanguinetti, F. Gini, M. S. Greco, and B. Himed, “Massive MIMO radar for target detection,” IEEE Transactions on Signal Processing, vol. 68, pp. 859–871, 2020.
[5] A. M. Ahmed, A. A. Ahmad, S. Fortunati, A. Sezgin, M. S. Greco, and F. Gini, “A reinforcement
learning based approach for multitarget detection in massive MIMO radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 5, pp. 2622–2636, 2021.

Profil du candidat :
This interdisciplinary project requires skills in statistical signal processing and machine learning, with specifical focus on Reinforcement Learning. Basic knowledge of radar principles may be useful but not required. Concerning the programming languages, the candidate should have a good knowledge of Matlab and possibly of Python.

Formation et compétences requises :
1) Statistics,
2) Reinforcement Learning,
3) Statistical Signal processing.

Adresse d’emploi :
Laboratoire des signaux et systèmes (L2S),
bât. Bréguet, 3, rue Joliot Curie,
91190 Gif-sur-Yvette.

Document attaché : 202206030915_PhD_Proposal_Fortunati.pdf