Postes/PostDocs/CDD
Sep 1 Mon 2025 Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : IMT Mines Alès (Ecole Nationale Supérieur des Mine
Durée : CDI
Contact : guyot@irit.fr
Date limite de publication : 2025-09-01 Contexte : Sujet :
Maître de Conférences en Apprentissage Machine et Contrôle de systèmes complexes Profil du candidat : Formation et compétences requises : Adresse d’emploi :
IMT Mines Alès (Ecole Nationale Supérieur des Mines d’Alès) Document attaché : 202409050837_fp_-_mdc_ceris_ia_et_ingenierie_fr-2.pdf Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : Brain-C Lab, Institute of Biology Paris-Seine (IBP
Durée : 12 months
Contact : lucile.megret@sorbonne-universite.fr
Date limite de publication : 2025-09-01 Contexte : Sujet :
Project: A 12 months position is immediately available in the Brain-C Lab in Paris for a bioinformatician at the Study Engineer (IE) level (post-master position). The selected candidate will work with a team of mathematicians, bioinformaticians, and neurobiologists on modeling time- and cell-resolved omics data to built computational models of molecular pathogenesis in neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), integrate data from other diseases such as Huntington’s disease (HD) and disseminate data via online platforms. The selected candidate will use BioGemix, our post-omics machine learning platform and related databases. This position is a unique opportunity to further develop expertise and skills in a multidisciplinary team and network of direct collaborators that cover systems modeling, database development, and cellular neurobiology for breakthrough in neurodegenerative disease research. Profil du candidat :
Profile: The candidates should hold a Master in Biofinformatic or a Master in Informatics and they should have no more than 3-4 years of post-master experience. The position is full time, on site, and candidates should have strong collaborative skills and commitment to team work along with strong ability to work independently in addition to strong interest for research. Formation et compétences requises :
• Training and required skills:
Experience working with NGS data and performing respective bioinformatic pipelines in order to process sequencing data.
• Programming autonomy on at least one of the following languages: python, R, C / C ++.
• Good knowledge of basic web technologies: PHP, MySQL, JavaScript, jQuery.
• Fluency on Ubuntu.
• Hands-on experience with in house server maintenance (Backup, shared space, and webserver).
• Scientific English essential • Skills in data visualization will be a plus.
• Basic knowledge in statistics and machine learning are desirable but not mandatory. Adresse d’emploi :
7 quai Saint Bernard 75005 Paris Document attaché : 202506271149_Annonce_bioinfo_IE.pdf Sep 26 Fri 2025 Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : Toulouse INP : ENSEEIHT/IRIT ou AgroToulouse/Dynaf
Durée : 3 à 5 ans
Contact : nicolas.dobigeon@enseeiht.fr
Date limite de publication : 2025-09-26 Contexte : Sujet :
Une chaire de professeur junior (CPJ) est ouverte au recrutement à Toulouse INP sur le thème de la “modélisation et apprentissage automatique pour l’agroécologie” (IA, modélisation, télédétection, agronomie…). Selon le profil du lauréat ou de la lauréate, deux rattachements sont possibles :
– à l’INP-AgroToulouse (enseignement) et à l’UMR DYNAFOR (recherche)
– à l’INP-ENSEEIHT (enseignement) et à l’UMR IRIT (recherche) Quel que soit le rattachement, l’objectif est que la personne contribue à créer de la synergie entre les deux écoles et les laboratoires, dans le cadre de la création d’une nouvelle formation transversale associée. Les candidatures sont ouvertes jusqu’au au vendredi 22 août 2025 à 16h00 (heure de Paris) sur la plateforme ODYSSEE :
https://odyssee.enseignementsup-recherche.gouv.fr/
Voir les instructions ici :
https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/cand_recrutement_enseignants_chercheurs_Odyssee.htm Des détails concernant le contexte et les objectifs de cette chaire sont disponibles sur l’appel à candidature :
https://www.inp-toulouse.fr/_attachment/campagnes-de-recrutement-enseignants-chercheurs-accordeon/Profil%202025_CPJ%20Mod%C3%A9lisation%20et%20apprentissage%20automatique%20pour%20l’agro%C3%A9cologie.pdf?download=true Profil du candidat :
Au terme de la période de 5 ans maximum, une titularisation dans le corps des Professeurs des Universités sera envisagée. Cette chaire est donc adaptée à des personnes ayant déjà 3 à 5 ans d’expérience après la thèse et dont le dynamisme permettrait de prétendre à un poste de Professeur à la fin d’une période de 5 ans, en supposant l’HDR soutenue. Elle est aussi ouverte à des personnes déjà titulaires de l’HDR. Dans ce cas, en fonction de l’expérience, la durée de la chaire pourrait être réduite à 3 ans avant une possible titularisation. Formation et compétences requises :
La thématique de la chaire est large et peut correspondre à des personnes venant du monde des sciences des données et du traitement des images, intéressées par l’agroécologie spatialisée comme domaine d’application privilégié. Elle s’adresse aussi à des agronomes / agroécologues ayant une bonne expérience en modélisation (statistique, mécaniste) spatialisée. Adresse d’emploi :
Toulouse INP Document attaché : 202507100908_Profil 2025_CPJ Modélisation et apprentissage automatique pour l’agroécologie.pdf Sep 30 Tue 2025 Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : CReSTIC / Institut Godinot / Siemens
Durée : 24 mois
Contact : Agnes.Delahaies@univ-reims.fr
Date limite de publication : 2025-09-30 Contexte : Sujet :
https://crestic.univ-reims.fr/uploads/emplois/Siemens_Post_Doc.pdf Profil du candidat : Formation et compétences requises : Adresse d’emploi :
Université de Reims Champagne-Ardenne (Reims, Troyes) Document attaché : 202505161710_Siemens_Post_Doc.pdf Oct 1 Wed 2025 Offre en lien avec l’Action/le Réseau : – — –/Doctorants Laboratoire/Entreprise : ETIS/CY Paris Cergy Université
Durée : 18 months
Contact : aikaterini.tzompanaki@cyu.fr
Date limite de publication : 2025-10-01 Contexte :
The position is funded by the prestigious EU Horizon PANDORA project, A Comprehensive Framework enabling the Delivery of Trustworthy Datasets for Efficient AIoT Operation. The main goal of the project is to contribute towards the creation of a dynamic AI pipeline in the context of IoT applications, focusing on i) the creation of synthetic but trustworthy data, and ii) on the development of AI algorithms for various tasks (from classic classification, regression and anomaly detection, to forecasting and predictive maintenance) taking into account the specific data characteristics of continuous data generation settings and leveraging the domain knowledge when available. Explainability is a fundamental property for rendering the systems reliable, as it can help i) enhance the acceptability of the system by the system experts and further aid in decision making, ii) optimize model performance (time and accuracy) by revealing the actual causes behind predictions, and iii) repair data acquisition processes or model training/updating by exposing errors and/or drifts that may arise through time. The project gathers over 20 academic and industrial partners, with real and challenging use-cases and thus provides a unique opportunity to contribute to cutting-edge research with significant real-world impact. Sujet :
Subject: Predictive maintenance (PDM) in industrial settings spans from identifying anomalies and categorizing failures in already observed data, to prognostically predicting the Remaining Useful Life (RUL) and the Failure Time (FT) of machines, appliances, etc., in the future. Typically such predictive tasks are implemented using Deep Learning and/or statistical analysis techniques, which may be complex to interpret, while their performance is challenged by multiple sources of errors (sensors’ tuning, aging, failures, etc.) and the non-stationary nature of IoT data streams. In particular, concept drifts can originate from the changes in the underlying data generation mechanism that reflects different states of the monitored system. In this context, the predictive performance of already trained models f(X,Y) (e.g., classification, regression) may start degrading after a certain point in time and hence models need to be adapted at the right frequency. However, not all types of changes in the joint probability distribution P(X,Y) have the same impact on model performance. In this respect, we need to distinguish between changes in the posterior probability distribution P(Y|X) (i.e., Model drifts) between input features X and the target variable Y, from class-conditioned data distribution changes P(X|Y) (i.e., likelihood drifts) and changes in the distribution of input features P(X) (i.e., covariate drifts). Clearly, not all types of changes of the joint probability distribution $P(X, y)$ influence predictive models in the same way and hence, they require different mitigation actions. In this project we are particularly interested in how data quality and concept drifts affect the performance of PDM tasks such RUL and FT. Moreover, besides developing performant, robust, and stable FT and RUL prediction algorithms through time, we are also interested in enhancing interpretability of their results. On the one hand, engineers need to know the root causes for a predicted machine failure at a time t in the future, so that they may take the best possible action towards preventing the failure to happen, or replace a machine in time before having to take the system down for replacement of the compromised machine. Such explanations should cover both the time parameter (why a failure will happen after a time interval) and the type of failure (why a specific type of error will happen). On the other hand, fine-grained explanations of the different types of concept drifts can guide data analysts to take timely, and informed actions for adapting the prediction algorithm to the observed concept drift. While explainability has been a major research interest in recent years, explanation methods for concept drift are still in their infancy. Some of the approaches aim for the detection and quantification of drift, its localization in space or its visualization, while others focus on feature-wise representations of drift. In this project, we aim to investigate actionable concept drift explanations, adding in the equation weak and strong signals for failure events. We believe that concept drift explanations constitute a form of actionable explanations responding to both aforementioned expert needs, and thus can be more valuable than standard feature importance explanations. Profil du candidat :
Responsibilities/Opportunities
Conduct high quality research in Explainable AI in non-stationary settings.
Develop novel algorithms and methodologies for Predictive Maintenance using IoT streams.
Publish in top-tier conferences and journals.
Collaborate with an interdisciplinary team of researchers and industry partners.
Participate in project meetings and contribute to the project’s management. Formation et compétences requises :
PhD in Machine Learning, AI, Data Science, Statistics, or a related field.
Strong background in at least one of the following: deep learning, continual learning, time series analysis, or predictive maintenance.
Experience with explainable AI (XAI) methods.
Proficiency in Python and relevant ML frameworks (PyTorch, TensorFlow, Scikit-learn).
Excellent publication record in Data management, Artificial Intelligence, Machine Learning, or IoT applications. Adresse d’emploi :
Equipe DATA&AI – ETIS Laboratory, CY Cergy Paris Université 33 Bd du Port, 95000 Cergy Document attaché : 202503241752_Post-Doc Position in Explainable AI in Industrial Settings_DATAIA_PARIS.pdf Oct 31 Fri 2025 Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : IMT Atlantique – Lab-STICC
Durée : 2 years
Contact : lucas.drumetz@imt-atlantique.fr
Date limite de publication : 2025-10-31 Contexte :
IMT Atlantique is looking for a postdoctoral fellow for 2 years starting as soon as possible before December 2025. The position is based on the Brest campus of the school. The fellow will join the Mathematical and Engineering Department of IMT Atlantique, and conduct his research wihtin the Odyssey Team Project studying Ocean Dynamics using data driven approaches, namely ML and AI techniques. The project is a collaboration with the Laboratoire d’Océanographie Physique et Spatiale (LOPS) of Univ. Brest. Covering more than 70 % of the surface of the Earth, the oceans play key roles for the regulation of the Earth climate (e.g., climate change) as well as for human societies (e.g., marine resources and maritime activities). Despite ever-increasing development of simulation and observation capabilities leading to ocean big data, our ability to understand, reconstruct and forecast ocean dynamics remains limited. Altimetry data and other geophysical measurements allow an improved understanding of ocean dynamics thanks to the various types of data acquired at the surface and inside of the ocean by different in situ sensors and satellite missions. A large number of different sensors exist, ranging from in situ floats to satellite data. However, even with all these acquisitions, the observation coverage of the 3D ocean remains very sparse, making prediction tasks challenging. In such a setting, it is also crucial to be able to produce uncertainties associated to the predictions, or even propose different evolution scenarios in ambiguous configurations. Sujet :
In this context, a national initiative within the Programme Prioritaire de Recherche (PPR) Océan et Climat aims at building data challenges centered around the exploitation of such datasets, and easy benchmarking of AI-based solutions [1]. One of those challenges to be launched by the end of 2025 will be centered around the probabilistic short term forecasting of oceanic variables at the global scale in the 3D ocean from sparse measurements. AI-based methods for this type of problems are starting to emerge but are not so mature as e.g. in weather forecasting, and in particular are usually deterministic [2, 3]. Thus, the objective of this postdoctoral project is to develop new AI native models to learn and propagate ocean dynamics, with an emphasis on:
• Handling incomplete and noisy data
• A probabilistic formulation of the problem, i.e. not just learning a mean value over space and time of the
parameters of interest, but also to generate and propagate uncertainties in the relevant oceanic variables over space in time.
• A method that scales to global oceanic states (≈ 10 6 variables or more) To this end, we propose to merge Gaussian Process (GP) regression [4], which is the basis of most operational techniques for sparse oceanic data interpolation and forecast (Optimal Interpolation) and Machine Deep Learning techniques. These kernel-based methods take advantage of the closed form Gaussian solution of the GP providing uncertainties at little cost. Despite these appealing properties, their performance has been modest because of limited expressivity due to ad-hoc choices of rigid kernels and associated parameters. We propose to enhance this class of probabilistic methods by leveraging flexible AI-based hyperparameterizations of these kernel methods to train data-driven priors for interpolation/forecasting from complete (e.g. simulation) data. Profil du candidat :
The candidate should hold a PhD in signal or image processing, machine learning, remote sensing or related fields. We are looking for strong candidates with the following skills:
• Machine/Deep learning, signal and image processing, applied mathematics, numerical methods
• Programming in Python (Numpy, scipy, matplotlib…), especially Pytorch
• Inverse problems in imaging, Bayesian Modeling, Kernel Methods
• Curiosity for or experience in applications to quantitative oceanography will be appreciated. Formation et compétences requises :
PhD in applied math/machine learning/image or signal processing Adresse d’emploi :
IMT Atlantique Technopôle Brest-Iroise CS 83818 29238 Brest Cedex 3 Document attaché : 202506161220_postdoc_ppr_ocean_climat_fiche.pdf Dec 31 Wed 2025 Offre en lien avec l’Action/le Réseau : – — –/Doctorants Laboratoire/Entreprise : CRISTAL Lab – UMR CNRS 9189/University of Lille
Durée : 24 months – Ideally
Contact : hayfa.zgaya-biau@univ-lille.fr
Date limite de publication : 2025-12-31 Contexte :
The IARISQ project, funded by the French National Research Agency (ANR), aims to develop advanced artificial intelligence (AI) models to predict the toxicity thresholds of airborne particles, taking into account their physico-chemical properties and environmental dynamics. The project combines AI, probabilistic modeling, fuzzy logic, and explainable AI (XAI) to build a robust decision support system for public health and environmental risk assessment. Sujet :
We are seeking a highly motivated postdoctoral researcher with strong expertise in machine learning and data science. The selected candidate will contribute to the design, implementation, and evaluation of predictive AI models for toxicity thresholds, with a focus on:
– Developing deep learning models (e.g., GANs, Transformers, TabNet)
– Managing uncertainty with probabilistic (e.g., GPR, Bayesian Neural Networks) and fuzzy logic approaches (e.g., Interval Type-2 Fuzzy Logic)
– Applying explainable AI techniques (e.g., SHAP, LIME, GrC) to identify influential variables
– Collaborating with environmental scientists and air quality experts
– Preparing scientific publications and sharing code (GitHub, open-source) Related Publications
The candidate will contribute to a project with a strong publication record in top-tier journals and conferences. Recent related publications include:
1. Idriss Jairi, Sarah Ben-Othman, Ludivine Canivet, Hayfa Zgaya-Biau, Explainable-based approach for the air quality classification on the granular computing rule extraction technique, Engineering Applications of Artificial Intelligence, 2024. (Q1, IF: 7.5, AI/Software) https://doi.org/10.1016/j.engappai.2024.108096
2. Idriss Jairi, Sarah Ben-Othman, Ludivine Canivet, Hayfa Zgaya-Biau, Enhancing Air Pollution Prediction: A Neural Transfer Learning Approach across Different Air Pollutants, Environmental Technology & Innovation, 2024. (Q1, IF: 6.7, Environmental Engineering) https://doi.org/10.1016/j.eti.2024.103793
3. Idriss Jairi, Amelle Rekbi, Sarah Ben-Othman, Slim Hammadi, Ludivine Canivet, Hayfa Zgaya-Biau, Enhancing particulate matter risk assessment with novel machine learning-driven toxicity threshold prediction, Engineering Applications of Artificial Intelligence, 2025. (Q1, IF: 7.5, AI/Software) https://doi.org/10.1016/j.engappai.2024.109531 Conference
Doctoral Consortium Participant, ECAI 2024 – European Conference on Artificial Intelligence, Santiago de Compostela, Spain – October 2024. https://anaellewilczynski.pages.centralesupelec.fr/ecai-2024-dc/accepted.html Profil du candidat :
PhD in Artificial Intelligence, Machine Learning, Data Science, or a closely related field.
– Strong experience in developing and evaluating deep learning models (e.g., GANs, Transformers, LSTM).
– Solid background in uncertainty modeling, explainable AI (XAI), or hybrid AI approaches is a plus.
– Excellent programming skills (Python, PyTorch or TensorFlow).
– Proven ability to conduct high-quality research, with publications in top-tier conferences or journals.
– Autonomy, creativity, and ability to work in a multidisciplinary environment (AI + environment + public health).
– Strong communication skills (oral and written) in English. Formation et compétences requises :
PhD in computer Science – Artificial Intelligence Adresse d’emploi :
https://www.cristal.univ-lille.fr UMR CRIStAL
Université de Lille – Campus scientifique
Bâtiment ESPRIT
Avenue Henri Poincaré
59655 Villeneuve d’Ascq
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Offres de thèses
Aug 31 Sun 2025 Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : CReSTIC / Institut Godinot / AQUILAB
Durée : 36 mois
Contact : Arnaud.BEDDOK@reims.unicancer.fr
Date limite de publication : 2025-08-31 Contexte : Sujet :
Cf. document pdf. Profil du candidat : Formation et compétences requises : Adresse d’emploi :
CReSTIC
Université de Reims Champagne Ardenne Document attaché : 202505121850_Appel_candidature_20250512.pdf Oct 15 Wed 2025 Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : LASTIG, IGN / Gustave Eiffel University
Durée : 3 years
Contact : valerie.gouet@ign.fr
Date limite de publication : 2025-10-15 Contexte :
PhD offer
Indexing and retrieval of visual contents in 3D point clouds at large scale – Application to spatialization
LASTIG Lab / IGN and Gustave Eiffel University / Paris great area, France All the details: https://agape-anr.github.io/docs/annonce_these_loc2D3D-EN.pdf Sujet :
At a glance The thesis project focuses on the spatialization of visual contents (both image and video contents) by the exploitation of 3D references at large scale. Without any a priori about geolocation, the problem is tackled by the retrieval of the most similar elements in the geolocalized reference. As visual content, we consider old photographs and footages made available from cultural institutions, and as 3D reference we exploit LiDAR data mapping the French territory, made available at the country scale by the French mapping agency (IGN). This PhD thesis has the ambition to address two challenging scientific problems: on the one hand, the description, matching and indexing of 2D(+t) and 3D data in a multi-date context where the scene has evolved over time, and on the other hand, the fast retrieval in very large volumes of data. The work will be carried out within the framework of the multidisciplinary project AGAPE, which addresses the discoverability and investigation in spatial iconographic heritage, and gathers seven leading partners specialized in visual and multimodal AI, Multimedia and Human-Computer Interaction as well as in Archives, History and Media. Keywords Computer Vision, Artificial Intelligence, Indexing and Retrieval, Vision Languages Models, Image analysis, 3D Point Clouds, Big Data, Geolocalization, Cultural Heritage. Profil du candidat :
How to apply Before July 14, 2025, please send to both contacts in a single PDF file the following documents: o A detailed CV
o A topic-focused cover letter
o Grades and ranks over the last 3 years of study
o The contact details of 2 referents who can recommend you Candidatures which do not respect these instructions will not be considered. Auditions will be conducted during period July 15-23; decision released no later than July 25. Formation et compétences requises : Adresse d’emploi :
IGN-ENSG, Université Gustave Eiffel
6-8 Av. Blaise Pascal, 77420 Champs-sur-Marne
FRANCE Document attaché : 202506021533_annonce_these_loc2D3D-EN.pdf Dec 31 Wed 2025 Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : ISIR – Institut des Systèmes Intelligents et de Ro
Durée : 36 mois
Contact : patrick.gallinari@sorbonne-universite.fr
Date limite de publication : 2025-12-31 Contexte :
AI4Science is an emerging research field that investigates the potential of AI methods to advance scientific discovery, particularly through the modeling of complex natural phenomena. This fast-growing area holds the promise of transforming how research is conducted across a broad range of scientific domains. One especially promising application is in modeling complex dynamical systems that arise in fields such as climate science, earth science, biology, and fluid dynamics. A diversity of approaches is currently being developed, but this remains an emerging field with numerous open research challenges in both machine learning and domain-specific modeling. Generative modeling is transforming machine learning by enabling the synthesis of plausible, high-dimensional data across modalities like text, images, and audio. A similarly profound shift is underway in the sciences, where generative deep learning is being leveraged to model complex physical dynamics governed by partial differential equations (PDEs)—especially in cases where traditional simulations are computationally expensive. The central goal of the PhD project is to investigate whether deep generative architectures—such as diffusion, flow-matching, or autoregressive transformer-based sequence models—can be designed to simulate, generalize, and interpolate physical dynamics across a wide range of parametric and multiphysics regimes. Building on recent advances in neural surrogate modeling, this research will aim to advance generalizable, cross-physics generative modeling. Sujet :
RESEARCH OBJECTIVES The overarching research question is: Can we develop generative models that learn structured, physically grounded representations of dynamical systems—enabling synthesis, adaptation, and generalization across physical regimes and multiphysics settings? It unfolds into several complementary directions: LATRENT GENERATIVE MODELS FOR PHYSICAL DYNAMICS The objective is to design generative models—such as diffusion, flow-matching, or autoregressive models—that learn compact and interpretable latent representations of spatiotemporal dynamics governed by PDEs. These models should: • Capture uncertainty and multimodality in solution trajectories.
• Generalize across parametric variations. LEARNING ACROSS MULTIPHYSICS SYSTEMS To enable transfer learning across heterogeneous physics, we will explore shared latent representations across families of PDEs:
• Using encode–process–decode frameworks.
• Applying contrastive or multitask training to uncover reusable physical abstractions.
• Designing models invariant to space/time resolution and units.
This direction builds toward foundation-like models that capture generalizable physics priors across simulation families. FEW-SHOT and IN-CONTEXT GENERALIZATION TO NEW PHYSICS To support scientific modeling in data-scarce settings, we will develop methods for few-shot generalization such as:
• Fine-tuning latent priors to new PDE systems using limited examples.
• Exploring meta-learning and prompt-based adaptation techniques (inspired by in-context learning in language models).
• Incorporating known physical constraints into the generative process.
The goal is to enable rapid and physically consistent adaptation to previously unseen dynamics with minimal data and supervision. Profil du candidat : Computer science or applied mathematics.
Master degree in computer science or applied mathematics, Engineering school. Formation et compétences requises :
Good programming skills. Background and experience in machine learning. Adresse d’emploi :
Sorbonne Université (S.U.), Pierre et Marie Campus in the center of Paris. The candidate will integrate the MLIA team (Machine Learning and Deep Learning for Information Access) at ISIR (Institut des Systèmes Intelligents et de Robotique). Document attaché : 202505191314_2025-05-01-PhD-Description-Generative-models-Physics.pdf Offre en lien avec l’Action/le Réseau : – — –/Doctorants Laboratoire/Entreprise : Laboratoire Informatique d’Avignon avec codirect
Durée : 3 ans
Contact : rosa.figueiredo@univ-avignon.fr
Date limite de publication : 2025-12-31 Contexte :
ANR project EVA – EValuating gender policies in academia through the Analysis of scientific collaboration networks. Sujet :
https://eva.univ-avignon.fr/wp-content/uploads/sites/34/2025/04/offre.pdf Profil du candidat :
• Master’s degree (or equivalent) in Computer Science, Applied Mathematics, Operations Research, or a related field.
• Strong ability to write and present research clearly.
• Proficiency in Python, R, Julia or C++, with experience in AI and optimization algorithms.
• Good understanding of graph theory, machine learning, and network analysis.
• Ability to work well in an interdisciplinary team.
• Proficiency in English is required, and knowledge of French is an advantage Formation et compétences requises : Adresse d’emploi :
LIA, Avignon Document attaché : 202504251721_offreThesis_EVA.pdf Offre en lien avec l’Action/le Réseau : – — –/– — – Laboratoire/Entreprise : LISTIC
Durée : 36 mois
Contact : yajing.yan@univ-smb.fr
Date limite de publication : 2025-12-31 Contexte : Sujet :
Satellite based remote sensing offers a unique source of information to monitor the environnement, with fine spatial resolution, wide coverage and frequent revisit. This enables
addressing the challenge of natural hazard monitoring and forecasting, which has a significant societal impact. The inverse modeling of surface displacement is one of the major techniques of exploring the subsurface feature of volcanoes. The traditional Monte Carlo direct search approaches are
computational resources and time consuming, thus cannot respond to operational needs. We will explore the potential of deep learning in volcanic inverse modeling with Interferometry
Synthetic Aperture Radar (InSAR) for operational monitoring and forecasting of volcanic hazards. The intrinsic ill-posedness of inversions in volcanology and limited amount of labeled InSAR data make this work challenging. We tackle the problem of volcanic model inversion, i.e. to estimate model parameters from surface displacement estimations issued from InSAR by solving an inverse problem. This Ph.D thesis will elaborate on our previous proof-of-concept work where a frugal ResNet model was deployed for the first time to estimate the volume change and depth of a spherical volcanic source (i.e. Mogi) from synthetic InSAR displacement fields. This ResNet model exhibits distinct advantages of computational efficiency over the state-of-the-art Monte Carlo direct search methods. For this thesis, the Ph.D student will use more sophisticated volcanic models (e.g. fracture, numerical boundary element models, etc.) allowing for simulations of displacement fields caused by more complex volcanic sources to further increase the generality of the previously proposed ResNet model. One main effort will be devoted to the improvement of the ResNet model prediction accuracy by increasing training data diversity (e.g. divers SAR
acquisition geometries, near field/far field and multi-resolution measurements) and by elaborating more adapted loss functions corresponding to appropriate model properties to optimize (e.g. combination of a loss function of estimated model parameters and a loss function of the reconstructed displacement field). These two latter actions also help minimize the ill-posedness. Real InSAR displacement measurements related to both intrusion and reservoir type worldwide volcanoes will be used to fine-tune the ResNet model trained by synthetic data for
further validation in real applications. Profil du candidat :
The Ph.D candidate should have good skills in machine learning. Knowledge in inverse problem or geophysics is appreciated. Formation et compétences requises : Adresse d’emploi :
LISTIC, 5 chemin de bellevue, 74944, Annecy-le-Vieux
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