Offre en lien avec l’Action/le Réseau : BigData4Astro/– — –

Laboratoire/Entreprise : LPC2E Orléans
Durée : 5 mois
Contact : cherry.ng-guiheneuf@cnrs-orleans.fr
Date limite de publication : 2025-01-31

Contexte :
Pulsars are highly magnetized, rapidly rotating neutron stars. Thanks to the extreme stability of pulsar rotation, pulsars are considered “cosmic clocks” with a wide range of astrophysical applications. Pulsar discoveries have directly resulted in two Nobel prizes (1974 and 1993). Collectively, pulsar-related referred articles have led to over 500,000 citations; the discovery of pulsars is therefore a gateway to new science. Although it has been over 50 years since the first pulsar was discovered in 1967, pulsar searching is still in its early stage and the discovery space remains largely unexplored. Neutron star population synthesis studies suggest that there are ~120,000 potentially observable pulsars in our Galaxy, although currently just over 3000 pulsars are known. Traditionally, single-dish radio telescopes can only focus on a single point in the sky, limiting the sky mapping speed and hence the number of pulsar discoveries. In addition, the very-low frequency range remains relatively unexplored.

Sujet :
The newly commissioned NenuFAR telescope in France opens a new window in the very-low frequency range between 10 and 85 MHz. This unique frequency range and the large field-of-view of NenuFAR thus make it a promising instrument to undertake an exhaustive pulsar survey in the low frequency regime. Since 2020, NenuFAR has been conducting a blind (untargeted) pulsar search above declination 39°. Over 4000 hours of data have been collected to date of which only ⅓ processed. During this internship, the trainee will help optimize the data processing pipeline (in python) with the goal of improving the throughput of the search. There is also the possibility of deploying the pipeline on the 28-petaflop Jean Zay High Performance Computing (HPC) cluster operated by IDRIS/CNRS. We will work on aspects of parallelization, portability and modularization of the code. The trainees will also have the opportunity to gain insight into radio astronomy as well as to make first-hand pulsar discoveries.

Profil du candidat :
We are looking for candidates with prior python programming experience and who want to further strengthen their computing profiles. Knowledge in astronomy is preferred but not obligated.

We are only able to employ students with permits to work in France. This includes European citizens as well as students (from any nationalities) who are currently enrolled in a French university.

Formation et compétences requises :
– python programming

– at least B1 level in English (the internship will be conducted primarily in English)

Adresse d’emploi :
This internship will be hosted by the ASTRO team at the LPC2E/CNRS in Orléans (3E AVENUE DE LA RECHERCHE SCIENTIFIQUE, CS 10065, 45071 ORLEANS CEDEX 2, FRANCE).

The main research interests of the group is on radio transients including pulsars, fast radio bursts as well as SETI. The ASTRO team boasts the largest pulsar research group in France and is closely connected to the Nançay Radio Astronomical Observatory in the forest of Sologne. The ASTRO team currently has 6 permanent staff, 1 postdoctoral researcher and 3 PhD students. We typically welcome 1 to 2 M2 interns in the summer. Accommodation can be arranged on the CNRS campus at roughly €400/month. Lunch at the CNRS cantine is subsidized.

Document attaché : 202412091422_M2-2025_Cherry.pdf

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

Laboratoire/Entreprise : U2IS, Ensta Paris
Durée : 6 months
Contact : elena.vanneaux@ensta-paris.fr
Date limite de publication : 2025-03-01

Contexte :

Sujet :
Context Reinforcement learning (RL) has been widely adopted in robotics for its ability to learn from
interaction with the environment through feedback. It enables robots to adapt to environmental changes
and optimize their behavior according to performance criteria not known in advance [6]. However, to use
RL-based controllers for safety-critical tasks, one should also ensure that nothing ”bad” occurs during the
training and deployment of RL agents. Indeed, autonomous vehicles should never drive off the highway,
robotic prostheses should never force their users’ joints past their range of motion, and drones should
never fall out of the sky. The vulnerability of standard RL-based controllers to failures has spurred
significant growth in research on safe RL in the past decade [2].
In this internship, we will focus on provably safe RL, that provides hard safety guarantees for both
training and operation [7]. Provably safe RL approaches can be categorized into preemptive and postposed shielding [1]. In the preemptive method, the agent can only choose from actions that have been
a priori verified as safe. However, if a preemptive shield is too conservative, i.e., it identifies only a
few actions from the action space as safe, the agent’s capabilities for exploring the environment are
significantly reduced, which can lead to lower overall performance [3]. In post-posed shielding, the
safety filter monitors the RL agent behavior. If the agent wants to take an unsafe action, the shield
replaces it with a fallback strategy. Post-posed shields are usually more computationally efficient than
preemptive. Also, they are often easier to use in dynamic environments, which we want to investigate in
this internship. Still, in dangerous scenarios, a shield forces the system to use a predetermined safe but
likely sub-optimal policy [1]. Hence, while guaranteeing safety, shielding often contradicts task efficiency.
This internship aims to balance safety and performance by developing provably safe RL algorithms with
the agent’s guaranteed near-optimal behavior.
In our proof-of-concept work [5], we propose a predictive safety shield for model-based reinforcement learning agents in discrete space. The safety shield updates the Q-function locally based on safe
predictions, which originate from a safe simulation of the environment model. This shielding approach
improves performance while maintaining hard safety guarantees. Our experiments on grid-world environments demonstrate that even short prediction horizons can be sufficient to identify the optimal path.
We observe that our approach is robust to distribution shifts, e.g., between simulation and reality, without requiring additional training. This internship aims to extend the proposed approach to dynamically
changing environments [4].

Goals The goals of the internship consist of
• exploring the state-of-the-art safety shields for reinforcement learning algorithms
• proposing a shield that ensures safe behavior in dynamically changing environments.
• testing the proposed approach in GridWorld and PacMan environments

References
[1] Mohammed Alshiekh, Roderick Bloem, R¨udiger Ehlers, Bettina K¨onighofer, Scott Niekum, and Ufuk
Topcu. Safe reinforcement learning via shielding. Proceedings of the AAAI Conference on Artificial
Intelligence, 32, 08 2017.
[2] Lukas Brunke, Melissa Greeff, Adam W. Hall, Zhaocong Yuan, Siqi Zhou, Jacopo Panerati, and
Angela P. Schoellig. Safe learning in robotics: From learning-based control to safe reinforcement
learning. Annual Review of Control, Robotics, and Autonomous Systems, 5(1):411–444, 2022.
[3] Kai-Chieh Hsu, Haimin Hu, and Jaime F. Fisac. The safety filter: A unified view of safety-critical
control in autonomous systems. Annual Review of Control, Robotics, and Autonomous Systems,
7(1):47–72, July 2024.
[4] Nils Jansen, Bettina K¨onighofer, Sebastian Junges, Alex Serban, and Roderick Bloem. Safe reinforcement learning using probabilistic shields (invited paper). Schloss Dagstuhl – Leibniz-Zentrum
f¨ur Informatik, 2020.
[5] Pin Jin. A safety filter for rl algorithms based on a game-theoretic mpc approach, 2024. PRE –
Research Project, ENSTA.
[6] Jens Kober and Jan Peters. Reinforcement Learning in Robotics: A Survey, pages 9–67. Springer
International Publishing, Cham, 2014.
[7] Hanna Krasowski, Jakob Thumm, Marlon M¨uller, Lukas Sch¨afer, Xiao Wang, and Matthias Althoff.
Provably safe reinforcement learning: Conceptual analysis, survey, and benchmarking. Transactions
on Machine Learning Research, 2023. Survey Certification.

Profil du candidat :

Formation et compétences requises :
Profile of a candidate. For this position, you should meet the following requirements:
• enrollment in a Master’s program or equivalent in computer science, applied mathematics science,
engineering, or related disciplines;
• rigorous knowledge in formal verification, control design, and reinforcement learning;
• excellent programming skills (Python);
• proficiency in spoken and written English;
The candidate will have to submit the documents following:
• a cover letter;
• a resume;
• a copy of diplomas, bachelor’s and master’s degree transcripts.
In case of a successful internship, a Ph.D. offer in ENSTA Paris might be proposed.

Adresse d’emploi :
828 Bd des Maréchaux, 91120 Palaiseau

Document attaché : 202412061053_Safety_for_AI__M2.pdf

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

Laboratoire/Entreprise : LISIC – Univ-Littoral
Durée : 6 months
Contact : esteban.bautista@univ-littoral.fr
Date limite de publication : 2025-03-01

Contexte :
Temporal graphs, representing interactions over time, are crucial for analyzing datasets in areas like Industry 4.0, cybersecurity, and social networks. Temporal graphs often exhibit periods of distinct activity regimes, making change point (CP) detection vital for tasks such as fault detection and prediction. However, the sparsity and irregularity of real-world temporal graphs make CP detection highly challenging, as current algorithms struggle to extract accurate patterns.

Sujet :
The internship aims to build upon recent works that allow to transform temporal graphs upon a spectral domain where comparing different periods of a temporal graph is easier. Yet such transform relies on the choose of graph and signal dictionaries that must be properly chosen to attain satisfactory detection accuracy. For signal dictionaries, we aim to compare different choices, like Haar, Walsh, and Boolean-based dictionaries, which are adapted to the binary and sparse nature of temporal graphs. For graph dictionaries, we aim to build custom dictionaries with user-defined motifs.

Profil du candidat :
This internship is directed at students with various backgrounds (computer science, data science, signal processing,
complex systems) but with a strong interest in data science and graphs. Interest in the theoretical aspects of machine learning and in Python development will a plus.

Formation et compétences requises :
Ing3 or M2 students

Adresse d’emploi :
Saint Omer, France

Document attaché : 202412060909_Data2Laws___M2_Internship.pdf

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

Laboratoire/Entreprise : LIFAT Université de Tours
Durée : up to 6 Months
Contact : nicolas.ragot@univ-tours.fr
Date limite de publication : 2025-03-01

Contexte :
The JUNON project, driven by the BRGM, is granted from the Centre-Val de Loire region through ARD program (« Ambition Recherche Développement ») which goal is to develop a research & innovation pole around environmental resources (agriculture, forest, waters…). The main goal of JUNON is to elaborate digital services through large scale digital twins in order to improve the monitoring, understanding and prediction of environmental resources evolution and phenomena, for a better management of natural resources. Digital twins will allow to virtually reproduce natural processes and phenomena using combination of AI and environmental tools.
JUNON will focus on the elaboration of digital twins concerning quality and quantity of ground waters, as well as emissions of greenhouse gases and pollutants with health effects, at the scale of geographical area corresponding to the North part of the Centre-Val-de-Loire region.

Sujet :
The Master Thesis/internship position will be focused on the prediction of water resources and pollutants in the air.
The goal will be to benchmark state of the art time series approaches and to propose new methods adapted to the specificities of the environmental data studied (multivariate time series). The benchmark on water resources relies on complex data with different seasonality and frequencies. Forecasting must be from short term to long term predictions. Regarding air pollutants, the benchmark is still to be elaborated.

Profil du candidat :
Academic level equivalent to a Master 2 in progress or Engineer in its last year in computer science

Formation et compétences requises :
– a good experience in data analysis and machine learning (in python) is required
– some knowledge and experiences in deep learning and associated tools is required
– some knowledge in time series analysis and forecasting will be highly considered
– curiosity and ability to communicate and share your progress and to make written reports and presentations
– ability to propose solutions
– autonomy and good organization skills

Adresse d’emploi :
Computer Science Lab of the Université de Tours (LIFAT), Pattern Recognition and Image Analysis Group (RFAI)
64 av. Jean Portalis
37200 Tours

Document attaché : 202412060859_Fiche de poste stage Junon.pdf

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

Laboratoire/Entreprise : LISIC – ULCO
Durée : 6 mois
Contact : esteban.bautista-ruiz@lip6.fr
Date limite de publication : 2025-03-01

Contexte :
Dans les systèmes de transport avec trajets programmés (trains, avions, bus, etc.), des questions fondamentales se posent : existe-t-il un itinéraire permettant de visiter tous les arrêts ? Quel est le plus rapide ? Comment maximiser le nombre de lieux visités dans un temps limité ? Ces problématiques relèvent du Temporal Graph Exploration Problem (TEXP), qui consiste à trouver un chemin temporel permettant de visiter tous les sommets d’un graphe aussi vite que possible. Bien que crucial pour la logistique, la cybersécurité ou la détection de fraudes, le TEXP est un problème NP-difficile, ce qui rend le calcul de solutions exactes impraticable pour de grands graphes.

Des algorithmes heuristiques et d’approximation existent pour résoudre le TEXP, mais ils ont du mal à trouver un bon compromis entre vitesse et qualité des solutions. Les Graph Neural Networks (GNNs) se sont montrés efficaces pour résoudre des problèmes combinatoires sur des graphes statiques, et des versions plus récentes permettent maintenant de gérer les graphes temporels. Pourtant, ces outils n’ont pas encore été utilisés pour aborder le TEXP.

Sujet :
Nous visons à aborder le problème TEXP sous l’angle de l’apprentissage automatique en nous appuyant sur un cadre récent non supervisé pour l’optimisation combinatoire. Plus précisément, nous cherchons à (1) exploiter ce cadre pour concevoir une fonction de perte, basée sur la méthode probabiliste d’Erdős, qui optimise les parcours respectant les contraintes temporelles ; et (2) explorer des architectures récentes qui font l’embedding des parcours temporels, offrant un biais plus adapté au TEXP que les GNN classiques.

Profil du candidat :
Étudiants en informatique, science des données, recherche opérationnelle, ou systèmes complexes, ayant un fort intérêt pour l’optimisation combinatoire et l’apprentissage automatique sur graphes.

Formation et compétences requises :

Pour postuler, merci d’envoyer un e-mail à

– esteban.bautista@univ-littoral.fr
– rym.guibadj@univ-littoral.fr

en joignant les documents suivants pour appuyer votre candidature :

• votre CV ;
• une lettre de motivation ;
• vos relevés de notes de la dernière année de Licence à la dernière année de Master (si disponible) ;
• deux lettres de recommandation ou les noms et moyens de contact de deux conseillers académiques.

Les candidatures seront examinées au fur et à mesure jusqu’à ce que le poste soit pourvu.

Adresse d’emploi :
LISIC laboratory – St Omer site

Document attaché : 202412051118_Internship_ML-Temporal-Graph-Exploration.pdf

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

Laboratoire/Entreprise : PRISME laboratory
Durée : 5 – 6 months
Contact : yassine.nasser@univ-orleans.fr
Date limite de publication : 2025-03-01

Contexte :
Archaeologists often face challenges in matching the relief-printed patterns found on ceramic sherds discovered during excavations. Identifying sherds created with the same patterning tool (wheel) plays a crucial role in understanding ancient trade networks and provides valuable insights into past civilizations. Traditional methods involve manually stamping the motifs followed by a meticulous visual analysis to verify if these patterns were produced by the same wheel, a process that is not only time-consuming but also labor-intensive. Recent advances in artificial intelligence present a unique opportunity to revolutionize fields like archaeology by automating recognition processes, thereby accelerating discoveries and improving analysis precision.
This internship is a continuation of the PRIA REMIA research project (Pattern Recognition through Artificial Intelligence), developed in partnership between the PRISME laboratory, LIFO, and the Archaeological Service of the City of Orléans. In this context, we aim to develop an automated/intelligent system to assist archaeologists in identifying relief-printed decorations on medieval ceramic shards.

Sujet :
Internship Objectives :
In this context, the internship aims to build on previous work in preprocessing and segmentation by proposing innovative approaches. The primary tasks will focus on:
 – Exploring state-of-the-art methods in few-shot learning, similarity learning, deep clustering, and texture transformer models.
 – Developing a novel method for identifying and clustering ceramic sherds decorated with the same wheel.
 – Integrating the developed solution into the existing system.
 – Drafting documentation for the developed solution.

Profil du candidat :
Required degree level: Bachelor’s + 4 or equivalent
Preferred degree: Master’s in IA, mathematics, applied mathematics, or computer science, or equivalent, with a strong motivation for applied research.

Formation et compétences requises :
Required Skills
 – Strong programming skills in Python, including proficiency with deep learning and machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).
 – Familiarity with Deep Learning & Computer Vision, including Vision Transformers, Contrastive Learning, Similarity Learning, Clustering, and Texture Analysis.
 – Solid understanding of mathematics, especially in linear algebra and optimization.
 – Strong analytical, modeling, and writing skills.

Adresse d’emploi :
Polytech Orléans, 12 rue de Blois 45100 Orléans, France

Document attaché : 202412051055_M2 Internship 2024-2025 .pdf