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École Thématique AstroInformatique 2023 et Hackathon AstroInfo AISSAI

Date : 2023-06-26 => 2023-07-07
Lieu : L’école aura lieu dans le Sud-Est de la France, hébergée par le Village de Vacances Igesa « Destremau »

École Thematique AstroInformatique 2023 et Hackathon AstroInfo AISSAI
La troisième édition de l’école thématique AstroInformatique aura lieu du 26 au 30 juin et sera suivie du Hackathon AstroInfo AISSAI du 3 au 7 juillet.
L’inscription est ouverte!
L’école
Cette école a pour but de rassembler les chercheur·e·s, ingénieur·e·s et doctorant·e·s autour des nouvelles technologies du traitement de données massives en Astrophysique.
Les cours se concentreront sur des présentations et Travaux Pratiques en traitement des données, Machine learning et Deep Learning.
Le programme est disponible ici.
L’école aura lieu dans le sud-est de la France au Village Vacances Igesa « Destremau ».
Plus de détails sur la page web de l’école.
Ne tardez pas à vous inscrire, le nombre de places est limité !
Le hackathon
Dans la continuité de l’École nous organisons le Hackathon Astro AISSAI.
Le hackathon s’articule autour d’un projet scientifique et se déroulera tout au long de la semaine. Cette deuxième semaine s’adresse à des personnes ayant un peu d’expérience et le nombre de participants sera limité à 20.
Si vous êtes intéressé·e, merci de vous inscrire également sur le site du Hackathon.
Appel à propositions de projet
Nous invitons les participants “seniors” à soumettre des propositions de hack dans le domaine de l’astrophysique et de l’apprentissage automatique. Nous accueillons tous les types de projets, à condition qu’ils impliquent plusieurs participants pendant toute la semaine sur diverses tâches et conduisent à un résultat publiable.
Les candidats retenus travailleront à l’avance avec les organisateurs locaux pour préparer le matériel de hack (mise en page du projet, données ouvertes, code existant, notebook de départ, etc.).
Une connaissance préalable de l’apprentissage automatique est préférable mais non obligatoire, car une équipe locale sera disponible pour travailler avec les participants.
Veuillez soumettre vos propositions sur le formulaire d’inscription avant le 1er mai, 23h59 AOE (Anywhere on Earth).
Attention
L’inscription au hackathon et la soumission de sujet se font sur le site dédié au hackathon : https://aissai-hackathon.astroinfo.in2p3.fr/.
Thematic School AstroInformatics 2023
The third edition of the AstroInformatics thematic school will take place from June 26th to 30th, followed by the AstroInfo AISSAI Hackathon from July 3rd to 7th.
Registration is open!
The school
The goal of this school is to bring together researchers, engineers and students around new technologies for processing massive data in astrophysics.
The courses will focus on presentations and practical work in data processing, machine learning, and deep learning.
The program is available here.
The school will take place in the southeast of France at the Village Vacances Igesa “Destremau”.
More details on the school web site.
Don’t delay in registering, the number of spots is limited!
The hackathon
In the continuity of the school, we are organizing the Astro AISSAI Hackathon. The hackathon is based on a scientific project and will take place throughout the week. This second week is intended for people with some experience, and the number of participants will be limited to 20.
If you are interested in participating, please also register on the Hackathon web site.
Call for project proposals
We invite senior participants to submit hack proposals in the field of astrophysics and machine learning. We welcome all types of projects, as long as they involve and engage several participants for the entire week on various tasks and lead to a publishable result.
The successful candidates will work with local organizers in advance to prepare hack material (project layout, open data, existing code, starting notebook, etc.).
Prior knowledge of machine learning is preferable but not mandatory, as a local team will be available to work with the participants.
Please submit your proposals on the registration form before May 1st, 23:59 AOE (Anywhere on Earth).
Attention
Registration for the hackathon and submission of topics must be done on the dedicated hackathon website: https://aissai-hackathon.astroinfo.in2p3.fr/

Lien direct

Notre site web : www.madics.fr
Suivez-nous sur Tweeter : @GDR_MADICS
Pour vous désabonner de la liste, suivre ce lien.

Postes IA Vision et IA Robotique à l’ENSTA Paris

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

Laboratoire/Entreprise : U2IS
Durée : 3 ans (tenure track)
Contact : goran.frehse@ensta-paris.fr
Date limite de publication : 2023-04-30

Contexte :
L’ENSTA Paris ouvre deux postes d’enseignant-chercheurs au sein de son laboratoire U2IS, dans le contexte de son intégration au sein de l’Institut Polytechnique de Paris et de son projet “Compétences et Métiers d’Avenir” en Intelligence Artificielle. L’enseignement sera dispensé à l’ENSTA Paris et dans les formations IP Paris.

Les candidat(e)s retenu(e)s bénéficieront d’un « pack d’accueil » d’une bourse de thèse, ou deux demi-bourses de thèses, pour engager des travaux de recherche dans leur domaine  dans les 18 mois qui suivront leur recrutement. Un salaire attractif sera proposé en adéquation avec l’expérience.

Sujet :
Le titulaire du poste intègrera l’U2IS pour y développer son groupe de recherche en cohérence avec la stratégie et l’unité et participer aux enseignements gérés par cette dernière.

Profil du candidat :
Deux profils sont recherchés:
– Enseignant-Chercheur, niveau maitre de conférence ou professeur, orienté « Vision et Intelligence Artificielle »
– Enseignant-Chercheur, niveau maitre de conférence ou professeur, orienté « Robotique et Intelligence Artificielle »

Formation et compétences requises :
Des candidatures « junior » ou « expérimentées » sont possibles. Un candidat expérimenté avec HDR pourra obtenir le titre de Professeur accordé par la commission des titres d’ENSTA Paris.
Un salaire attractif sera proposé en adéquation avec le profil.

Adresse d’emploi :
Direction de l’unité d’informatique et d’ingénierie des systèmes (U2IS) ENSTA Paris
828, Boulevard des Maréchaux, 91762 Palaiseau Cedex

Document attaché : 202303291741_ENSTA-U2IS-EC-Robotique-Vision-IA-2023.pdf

Vers la prédiction des compositions d’équipe optimales

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

Laboratoire/Entreprise : Greyc/Skriners
Durée : 6 mois
Contact : albrecht_zimmermann@gmx.net
Date limite de publication : 2023-04-30

Contexte :
L’utilisation de méthodes informatiques pour analyser les données sportives donne aux praticiens (entraîneurs, agents, athlètes eux-mêmes) des outils puissants pour prendre des décisions plus objectives lorsqu’il s’agit d’une variété de questions qui se posent dans le sport
professionnel.
La société Skriners propose déjà un outil d’aide à la décision pour l’acquisition ou le remplacement de joueurs, basé sur des statistiques sophistiquées dérivées d’enregistrements vidéo de matchs. Skriners est un logiciel SaaS destiné aux professionnels du sport, qui leur permet de comparer, recommander et gérer des joueurs en fonction de critères statistiques.
Grâce à sa base de données complète, Skriners peut également aider à dénicher des talents prometteurs. Le logiciel propose également une fonctionnalité de gestion d’effectifs. Cette aide à la décision se limite pour l’instant aux joueurs individuels, sans tenir compte des coéquipiers ni des informations éventuelles sur les adversaires.
À long terme, l’outil doit être enrichi pour suggérer automatiquement des compositions d’équipe, sur la base des joueurs disponibles, de la stratégie de match envisagée, des informations sur l’équipe adverse, etc. Cela nécessitera de prendre en compte les synergies entre les
joueurs, ainsi que les performances de certains joueurs dans des systèmes défensifs ou offensifs donnés.

Sujet :
Le travail à effectuer dans le cadre de ce stage jettera les bases de cette recherche future, en explorant si et comment les travaux existants sur la chimie des équipes [1], le contexte de la performance des joueurs [2], et l’identification automatique des formations tactiques [3] peuvent être appliqués aux données actuellement disponibles à Skriners. Sur la base de cette évaluation, le stagiaire commencera à implémenter et à appliquer ces techniques aux données afin d’obtenir des statistiques supplémentaires, ou identifiera la manière dont les données et/ou
les méthodes doivent être adaptées.

[1] Bransen, Lotte, and Jan Van Haaren. “Player chemistry: Striving for a perfectly balanced soccer team.” arXiv preprint arXiv:2003.01712 (2020).
[2] Bransen, Lotte, Pieter Robberechts, Jesse Davis, Tom Decroos, Jan Van Haaren, Angel Ric, Sam Robertson, and David Sumpter. “How does context affect player performance in football?.” (2020).
[3] Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S. and Matthews, I., 2014, December. Large-scale analysis of soccer matches using spatiotemporal tracking data. In 2014 IEEE international conference on data mining (pp. 725-730). IEEE.

Objectifs
– Évaluer l’applicabilité des méthodes existantes aux données disponibles à Skriners
– Évaluer les besoins et les sources possibles de données supplémentaires
Activités
– Se familiariser avec les données dont dispose Skriners
– Se familiariser avec les travaux existants dans la littérature
– Identifier s’il existe des données qui seraient nécessaires mais qui sont actuellement manquantes
– Implémenter et appliquer les méthodes existantes aux données, en générant des statistiques supplémentaires
– Identifier des sources de données supplémentaires

Profil du candidat :
Étudiant en INFORMATIQUE ou en STATPS.
Les candidats sont encouragés à postuler dès que possible.

Formation et compétences requises :
Des connaissances en programmation, ainsi qu’en apprentissage automatique/exploitation de données ou en statistiques sont nécessaires.

Adresse d’emploi :
GREYC CNRS UMR 6072
Team CODAG – Contraintes, Ontologies, Data mining, Annotations, Graphes
Université de Caen Normandie
14000 Caen, France

Skriners
38 rue de Metz
92000 Nanterre

Document attaché : 202303291007_sujet de stage Skriners.pdf

Multiscale non-linear deep learning strategies to increase the spatial resolution of Land Surface Temperature

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

Laboratoire/Entreprise : IMT Atlantique
Durée : 10 months
Contact : carlos.granero-belinchon@imt-atlantique.fr
Date limite de publication : 2023-08-01

Contexte :
Nowadays, several operational Land Surface Temperature (LST) products are available but limitations remain, notably because there is still a trade-off between spatial and temporal resolutions. Thermal sensors such as MODIS or Sentinel 3 (1km spatial resolution) provide a high revisit (daily) and ASTER or the LANDSAT series provide a low revisit with high spatial resolution (around 16 days at 90 m and 100 m). Consequently, upscaling the spatial resolution helps improving the data fusion between different sensors, the generation of LST temporal series as well as a finer-scale analysis for different applications such as the monitoring of vegetation stress, forest fires or urban heat islands among others.

A large body of research has addressed this challenge with sharpening or disaggregation methods that are based on statistical relationships between high spatial resolution products and LST at low spatial resolution (Granero-Belinchon et al. 2019). However, these statistical approaches lead to limitations such as the need of high resolution products acquired in the same area and close in time, or scale invariant hypotheses which sometimes are not adapted.

Inspired by the existing research at the interface between AI and remote sensing, new AI-models continue to appear for the processing of spaceborne images, and more precisely for super-resolution applications, notably with CNNs (Convolutional Neural Networks) and GANs (General Adversarial Networks) (Brodu et al. 2017, Gargiulo et al. 2019).

Nguyen et al. 2022 showed that important improvements are still needed to correctly adapt CNNs for LST super resolution to overcome the scale-invariance hypothesis and the blurring effect. Thus, the inclusion of a physical information can lead to better performances for LST super resolution.

Sujet :
This proposal focuses on the MODIS sensor due to the large state-of-the-art available on this mission, the dataset already processed by the involved partners and the recent studies on this sensor dealing with the super resolution of its LST previously mentioned.

Following (Brodu et al. 2017) or (Gargiulo et al. 2019), a first approach consists in combining high resolution information in the VNIR domain (NDVI for example) with coarse resolution LST to train the model for super-resolution. We call this approach Dual image super resolution (DISR). The main advantage of this approach is the direct use of high resolution information for upscaling LST.

For Single image super resolution (SISR), learning will be performed at degraded resolution. Thus for example for LST upscaling from 1 km to 250 m, training will be done from 4 km to 1 km. This strategy is commonly used when no ground truth is available at the desired resolution (in our case LST
at 250 m), see (Nguyen et al. 2022). Consequently, a scale-invariance hypothesis is assumed, i.e. the learned model from 4 km to 1 km is valid when upscaling LST from 1 km to 250 m. However, scale-invariance is not necessarily exact and so this hypothesis must be corrected. We plan to overcome it by studying the statistical evolution of LST and reflectives indices such as NDVI across the involved scales and different landscapes (a hundred of meters to several kilometers) in order to deduce physical
laws allowing us to correct the scale invariance hypothesis of current AI super resolution methods in remote sensing. For doing so, LANDSAT 9 or ASTER data will be used.

Profil du candidat :
Candidates are expected to have a PhD in Deep Learning/Machine learning with strong experience in Neural Networks. Ideally, the candidate will have previous experience in remote sensing and would have shown strong interest on these topics during her/his PhD or previous postdocs.

Formation et compétences requises :
Good skills in python, pytorch, pytorch lightning are also required, as well as a background in teamwork. Previous experience in a multidisciplinary research team will also be considered as positive.

Adresse d’emploi :
The Postdoc will work in collaboration with Carlos Granero-Belinchon and Lucas Drumetz from IMT Atlantique, Aurélie Michel and Xavier Briottet from Onera Toulouse, Thomas Corpetti from CNRS and Julien Michel from CNES. Thus, the research team is composed by physicist, and researchers on artificial intelligence, signal and image processing and remote sensing from different laboratories, leading to a multidisciplinary project. Moreover, the postdoc will develop within the OSE research team at IMT (https://cia-oceanix.github.io/) which is a dynamic research group on image processing
and artificial intelligence for the study of the environment.

The post-doctoral position is a one-year full-time appointment starting during 2023. Gross salary will depend on the experience of the candidate, up to approx. 35,000 €/year. The candidate will also benefit from French social insurance, and will have up to 45 days of annual leave. The candidate will be able to benefit up to 90 days of remote working per year.

The candidate will be based at the IMT Atlantique Campus (Brest) in a dynamic and stimulating working environment at five minutes walking from the beach.

Within the framework of the ANR Chair OCEANIX the postdoc will have access to compute servers : Datarmor and servers from OSE at IMT Atlantique.

Teaching activities at IMT Atlantique will also be proposed to the postdoc, mainly in signal processing, computer vision and artificial intelligence. These actvities, which imply an additional salary, will not be mandatory.

Motivated candidates should send a CV and a motivation letter to: carlos.granero-belinchon@imt-atlantique.fr.

The Postdoc is expected to start in 2023.

Document attaché : 202303270947_IR_CNES_TOSCA.pdf

Ouverture de postes en informatique à l’EPITA sur 5 sites pour la rentrée 2023

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

Laboratoire/Entreprise : Laboratoire de recherche de l’EPITA (LRE)
Durée : CDI
Contact : pierre.parrend@epita.fr
Date limite de publication : 2023-04-30

Contexte :
L’EPITA ouvre plusieurs postes d’enseignant·e·s-chercheur·e·s en informatique à temps complet, pour un recrutement au plus tard en début d’année scolaire 2023-2024.

Sujet :
Venez consolider nos équipes et axes de recherche (https://www.lre.epita.fr/) sur les thématiques suivantes :

– Sécurité des logiciels et des architectures :
– Détection d’attaques de sécurité (apprentissage)
– Logiciels malveillants et rétroingénierie
– Cryptographie et blockchain
– Systèmes :
– Systèmes d’exploitation et noyaux
– Informatique en nuage et virtualisation
– Systèmes embarqués
– Robotique
– Science et ingénierie des données
– Intelligence artificielle :
– Extraction de connaissances
– Apprentissage automatique apprentissage profond
– Traitement du langage naturel
– et autres sous-domaines de l’IA
– Traitement d’images, reconnaissance des formes, vision
– Automates et leurs applications (dont vérification et synthèse),
– Logiciel et performance (dont HPC, GPU).

Profil du candidat :
Profil MCF ou profil HDR ou très bientôt HDR.

Il n’est pas formellement nécessaire d’avoir la qualification aux postes de maître·sse de conférences ou de professeur·e des universités pour pouvoir postuler.

Formation et compétences requises :
Les informations précises concernant ces postes et le lien pour nous transférer votre dossier de candidature sont disponibles ici :

– https://tinyurl.com/PosteEpitaMCF pour les profils MCF,
– https://tinyurl.com/PosteEpitaHDR pour les profils HDR ou très bientôt HDR.

La date limite de candidature est le 21 avril 2023.

(La procédure de recrutement est lisible ici : https://tinyurl.com/ProcedureRecrutement2023)

Adresse d’emploi :
Les postes sont à pourvoir sur les sites de :

– Paris (Kremlin-Bicêtre et Campus Cyber à la Défense)
– Lyon
– Rennes
– Strasbourg
– Toulouse.

Accueil

L’EPITA est une école privée d’ingénieurs en informatique avec l’accréditation CTI depuis 2007, évaluée par le Hcéres (la dernière vague était en 2017-2018, la prochaine est en 2024-2025), et rattachée à l’École Doctorale “EDITE de Paris” (ED 130).

Postdoc position in computational statistics and machine learning

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

Laboratoire/Entreprise : LTCI, Telecom Paris, Institut Polytechnique de Par
Durée : 2 years
Contact : pavlo.mozharovskyi@telecom-paris.fr
Date limite de publication : 2023-04-25

Contexte :
2-year postdoc position is available at the Image, Data and Signal department of Telecom Paris (https://www.telecom-paris.fr/) – one of the leading French engineering schools, a member of Institut Polytechnic de Paris (https://www.ip-paris.fr/).

Sujet :
The position is within the project LS-Depth-CaP funded by the Starting Grant of the French National Agency for Research in category Artificial Intelligence (ANR JCJC, CE23). The successful candidate is expected to conduct research on the topics including either or both theoretical and computational constituents focused on development of large-scale and robust statistical and machine learning methodology.

Profil du candidat :
Expected qualifications of the successful candidate:
– PhD (or equivalent) degree in statistics / data science / machine learning / artificial intelligence.
– Knowledge of programming in languages of machine learning: R / Python, C / C++, or similar.
– A good command of English.

Formation et compétences requises :
To candidate, following documents:
– Motivation letter.
– Curriculum vitae.
– Name(s) / email(s) of at least two references.
– Any other element(s) considered by the candidate useful for the application.
are to be uploaded at:
https://institutminestelecom.recruitee.com/o/postdoctorante-ou-postdoctorant-en-statistique-computationnelle-et-machine-learning-a-telecom-paris-cdd-de-24-mois

Adresse d’emploi :
Telecom Paris
19 place Marguerite Perey, F-91120, Palaiseau, France
https://institutminestelecom.recruitee.com/o/postdoctorante-ou-postdoctorant-en-statistique-computationnelle-et-machine-learning-a-telecom-paris-cdd-de-24-mois

Postdoctoral position on machine learning based eddy closures for ocean models

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

Laboratoire/Entreprise : Institut des Geosciences de l’Environnement, Gren
Durée : 12 months (renewable
Contact : Julien.Lesommer@univ-grenoble-alpes.fr
Date limite de publication : 2023-04-25

Contexte :
Mesoscale eddies are essential oceanic processes and their effect needs to be accurately represented in ocean components of climate models. In these models, the representation of mesoscale eddy processes affects the simulated means states, but also the overall variability and the response to changing conditions. Yet, because the spatial scales of mesoscale eddies are not explicitly represented in most ocean components of climate models, their effect is accounted for by subgrid closures.

The design of eddy closures for ocean models is an active field of research. With the development of scientific machine learning and its applications to fluid simulations, several eddy closures based on deep learning have been proposed (see Zanna and Bolton 2021). However, to date there has been no systematic evaluation of the impact of these new closures in full-scale realistic simulations. An important question is in particular whether their performance can be easily transferred from one ocean model to another.

Sujet :
The general mission is to conduct research work investigating the impact of machine learning based mesoscale eddy closures in ocean circulation models. The selected candidate will contribute to the M2LINES international project.

The selected candidate will contribute to a joint study aiming at analyzing the impact of several machine learning based eddy closures across different ocean models as part of the M2LINES international project. The work will specifically focus on the scheme proposed by Guillaumin and Zanna (2021) and its impact in the NEMO and MOM6 ocean circulation models. The selected candidate will be in charge of defining a test bed (simulation protocols, evaluation metrics) for assessing the impact of eddy closures in the NEMO 1/4° global ocean model (eORCA025). The work will then focus on refining the implementation of the Guillaumin and Zanna (2021) scheme in the NEMO ocean model and on performing a series of (ocean-only) model experiments. He/she will then analyze the results and contribute to the comparison with a companion effort with the MOM6 ocean model.

The work will be developed and implemented in close coordination with the MOM6 team, as part of the M2LINES collaboration. An important part of the work is therefore the participation in the M2LINES project activities (group meetings, seminars, etc). Regular visits to LOCEAN in Paris will also be required. The selected candidate will be expected to monitor upcoming publications, to write scientific articles, to present results in international conferences and to the relevant NEMO working groups (https://forge.nemo-ocean.eu/wgs).

Profil du candidat :
The selected candidate will hold a PhD in physical oceanography or in computational fluid dynamics, or computer science.

Formation et compétences requises :

The selection will be based on the following scientific and technical criteria: experience in geoscientific modeling, understanding of oceanic processes, experience Fortran and Python coding, experience in scientific writing, experience with one the prominent machine learning libraries (PyTorch, TensorFlow) (not compulsory); motivation to disseminate scientific results; ability to work within a team and in an international context.

The selection panel will also consider the gender balance of the entire research team.

Adresse d’emploi :
Institut des Géosciences de l’Environnement, Maison Climat Planète, 70 rue de la Physique, Domaine Universitaire, 38400 St Martin d’Hères

More information : https://lesommer.github.io/2023/02/15/postdoc-eddy-params-ml/

Please contact : julien.lesommer@univ-grenoble-alpes.fr and julie.deshayes@locean.ipsl.fr

Review of applications will begin immediately and continue until the position is filled.

Research engineer position on hybrid AI/physics ocean modeling

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

Laboratoire/Entreprise : Institut des Geosciences de l’Environnement, Gren
Durée : 18 months (renewable
Contact : Julien.Lesommer@univ-grenoble-alpes.fr
Date limite de publication : 2023-04-25

Contexte :
The combination of machine learning with scientific computing is an active area of research which is expected to improve geoscientific models and their integration into broader numerical systems, such as climate models and operational forecasting systems. A key practical question for these models is to define how machine learning components can be encoded and maintained into pre-existing legacy codes, written in low level abstraction languages (as FORTRAN). Several practical options exist, each coming with pros and cons. Neural networks may for instance directly be implemented in FORTRAN, one could alternatively use the C/C++-bindings of specific machine learning libraries or more generic high level coupling interfaces. But the trade-offs between these different strategies are usually model and use-case specific.

The NEMO ocean / sea-ice model (https://www.nemo-ocean.eu) and the CROCO ocean model (https://www.croco-ocean.org) are two important tools for the oceanographic community, in particular in the context of operational forecasting systems and european Earth System Models. Their development roadmaps involve the definition of sustainable interfaces for trainable components to be leveraged on-line during model simulations. A working group dedicated to machine learning related developments has been set-up as part of the NEMO development team.

Sujet :
The selected candidate will be in charge of providing quantitative information and developing practical solutions for a sustainable implementation of trainable components into the NEMO and the CROCO ocean models. This will involve defining benchmark use-cases of machine learning based components in ocean models. These will be based for instance on subgrid parameterizations already developed as part of the M2LINES project. The selected candidate will define quantitative metrics for intercomparing the different available options for coupling AI-based trainable components and legacy ocean models, and implement several options into the NEMO and CROCO ocean models. Possible options may include Infero (https://github.com/ecmwf-projects/infero), ICCS Fortran ML Bridge (https://github.com/Cambridge-ICCS/fortran-ml-bridge), HPE SmartSim (https://github.com/CrayLabs/SmartSim), Melissa (https://gitlab.inria.fr/melissa) or OASIS (https://oasis.cerfacs.fr/en/). The work will then involve performing systematic intercomparison based on realistic model simulations to be performed on HPC resources. The selected candidate will then write reports on the results and present the outcome of the work to relevant working group and project meetings. He/She will participate also in the discussions and meetings of the M2LINES and MEDIATION projects.

Profil du candidat :
The selected candidate will hold a MSc in computer science, engineer or PhD.

Formation et compétences requises :
The selection will be based on the following scientific and technical criteria: demonstrated experience in High Performance Computing; demonstrated experience in Fortran/C/C++ and Python coding; demonstrated experience in (at least) one of the prominent machine learning frameworks (PyTorch, TensorFlow,… ); basic understanding of Computational Fluid Dynamics and subgrid closures for fluid flows;
experience in running atmospheric, ocean circulation or climate models (not compulsory); demonstrated ability to work within a team.

The selection panel will also consider the gender balance of the entire research team. Junior candidates with a fresh title are also welcome.

This position may help you build a curriculum in the very active domain of hybridization between Numerical Simulation and Deep Learning (ML4Sci)

Adresse d’emploi :
Institut des Géosciences de l’Environnement, Maison Climat Planète, 70 rue de la Physique, Domaine Universitaire, 38400 St Martin d’Hères

More information : https://lesommer.github.io/2023/02/15/research-engineer-ml/

Please contact : julien.lesommer@univ-grenoble-alpes.fr and jurelie.albert@univ-grenoble-alpes.fr

Review of applications will begin immediately and continue until the position is filled.

CfP The 34th International Conference on Database and Expert Systems Applications – DEXA2023

Date : 2023-08-28 => 2023-08-30
Lieu : Penang, Malaysia

**** IMPORTANT DATES ****
Paper submission: 07 April 2023 (SHARP – FINAL)
Notification of acceptance: 10 May 2023
Camera-ready copies due: 1 June 2023
Conference days: 28-30 August 2023

Papers submission: https://equinocs.springernature.com/service/DEXA2023

**** PUBLICATION ****
All accepted DEXA2023 papers will be published by Springer in their Lecture Notes in Computer Science (LNCS). LNCS volumes are indexed in Scopus; EI Engineering Index; Google Scholar; DBLP; etc. and submitted for indexing in the Conference Proceedings Citation Index (CPCI), part of Clarivate Analytics’ Web of Science. Selected high-quality papers, after revision and extension, will be invited to be published, in a special issue of Knowledge and Information Systems (KAIS), Springer (IF = 3.161) and Transactions of Large Scale Data and Knowledge Centered Systems (TLDKS), Springer.

**** SCOPE ****
Database, information, and knowledge systems have always been a core subject of computer science. The ever increasing need to distribute, exchange, and integrate data, information, and knowledge has added further importance to this subject. Advances in the field will help facilitate new avenues of communication, to proliferate interdisciplinary discovery, and to drive innovation and commercial opportunity. Since 1990, DEXA has been an annual international conference which showcases state-of-the-art research activities in database, information, and knowledge systems. DEXA provides a forum to present research results and to examine advanced applications in the field. The conference and its associated workshops offer an opportunity for developers, scientists, and users to extensively discuss requirements, problems, and solutions in database, information, and knowledge systems.
DEXA 2023 invites research submissions on all topics related to database, information, and knowledge systems including, but not limited to the points in the list below. We also welcome survey papers, provided that the survey fills a void or goes beyond existing overview papers.
– Acquisition, Modelling, Management and Processing of Knowledge
– Authenticity, Privacy, Security, and Trust
– Availability, Reliability and Fault Tolerance
– Big Data Management and Analytics
– Consistency, Integrity, Quality of Data
– Constraint Modelling and Processing
– Cloud Computing and Database-as-a-Service
– Database Federation and Integration, Interoperability, Multi-Databases
– Data and Information Networks
– Data and Information Semantics
– Data Integration, Metadata Management, and Interoperability
– Data Structures and Data Management Algorithms
– Database and Information System Architecture and Performance
– Data Streams, and Sensor Data
– Data Warehousing
– Decision Support Systems and Their Applications
– Dependability, Reliability and Fault Tolerance
– Digital Libraries, and Multimedia Databases
– Distributed, Parallel, P2P, Grid, and Cloud Databases
– Graph Databases
– Incomplete and Uncertain Data
– Information Retrieval
– Information and Database Systems and Their Applications
– Mobile, Pervasive and Ubiquitous Data
– Modelling, Automation and Optimisation of Processes
– NoSQL and NewSQL Databases
– Object, Object-Relational, and Deductive Databases
– Provenance of Data and Information
– Semantic Web and Ontologies
– Social Networks, Social Web, Graph, and Personal Information Management
– Statistical and Scientific Databases
– Temporal, Spatial, and High Dimensional Databases
– Query Processing and Transaction Management
– User Interfaces to Databases and Information Systems
– Visual Data Analytics, Data Mining, and Knowledge Discovery
– WWW and Databases, Web Services
– Workflow Management and Databases
– XML and Semi-structured Data

**** SUBMISSION GUIDELINES ****
Authors are invited to electronically submit original research contributions or experience reports in English. DEXA will accept submissions of both short (up to 6 pages) and full papers (up to 15 pages including references and appendixes). DEXA reserves the right to accept submitted full papers only as short papers, in which papers describe interesting and innovative ideas which still require further technical development.
Any submission that significantly exceeds length limits or deviates from formatting requirements may be rejected without review.

*** SUBMISSION PROCEDURE ***
Papers submission will be managed using EquinOCS Springer Nature Conference Proceedings Submission System.
Authors should consult Springer’s authors’ instructions (https://www.springer.com/gp/computer-science/lncs/conference-proceedings…) and use the proceedings templates, either for LaTeX or for Word, for the preparation of their papers.
Once you click on the submission link (https://equinocs.springernature.com/service/DEXA2023), you will be guided to the EquinOCS Login page, which will be open in your browser. Click on the button “Submit now”. This will guide you directly to the paper submission process. If you already have an account at EquinOCS you will be asked to Login. After Login you will be guided to the start page where you can start with your submission. If you do not have an account at EquinOCS yet, please follow the registration process. Once your Account has been created, an email will be sent to the email you have stated in the registration process. Please follow the instructions in this email to activate your account and start your submission.
Please refer to EquinOCS user guide (https://support.springernature.com/en/support/solutions/articles/6000245…) for more information.

**** REVIEW PROCESS ****
Submitted papers will be carefully evaluated based on originality, significance, technical soundness, and clarity of exposition.
Duplicate submissions are not allowed and will be rejected immediately without further review.
Authors are expected to agree to the following terms: “I understand that the submission must not overlap substantially with any other paper that I am a co-author of or that is currently submitted elsewhere. Furthermore, previously published papers with any overlap are cited prominently in this submission.”
Questions about this policy or how it applies to a specific paper should be directed to the PC Co-chairs.

**** ACCEPTED PAPERS ****
All accepted conference papers will be published in a volume of “Lecture Notes in Computer Science” (LNCS) by Springer Verlag. Authors of all accepted papers must sign a Springer copyright release form. Papers are accepted with the understanding that at least one author will register for the conference to present the paper. Authors of selected papers presented at the conference will be invited to submit extended versions of their papers for publication in Knowledge and Information Systems (KAIS), Springer (IF = 3.161) and Transactions of Large Scale Data and Knowledge Centered Systems (TLDKS), Springer. The submitted extended versions will undergo a further review process.

**** Program Committee Chair ****
– Christine Strauss, University of Vienna, Austria
– Toshiyuki Amagasa, University of Tsukuba, Japan

Program Committees please refer to DEXA2023 website

For further inquiries, please contact dexa@iiwas.org

Lien direct

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PhD position in Deep Neural Networks with Dempster Shafer Theory (Fully funded)

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

Laboratoire/Entreprise : LGI2A
Durée : 3 ans
Contact : david.mercier@univ-artois.fr
Date limite de publication : 2023-05-23

Contexte :
Developing deep evidential networks in more depth by exploiting methods developed at LGI2A allowing one to consider finer knowledge about the quality, the dependence of information or the ignorance in predictions.

Candidate before May 2023

Sujet :
Deep neural networks (DNNs) refer to predictive models that exploit multiple layers of artificial neurons to compute a prediction [1,4]. In the original version, the layers are sequential and each neuron in a layer is connected with neurons in the previous layer. Many other alternative architectures have been proposed to adapt DNNs to solve specific and complex problems.

On the other hand, a theory called Dempster-Shafer theory of belief functions, or theory of evidence [15], has emerged as a rich and flexible generalization of the Bayesian probability theory, able to deal with imperfect (uncertain, imprecise, …) information. It is notably used in a growing number of applications such as classification (e.g. [2]), clustering (e.g. [3,7]) or information fusion (e.g. [5,13]).

Recent works [6,16,17] have shown the interest of enriching a DNN with an additional distance-based Dempster Shafer layer [2] for predicting belief functions. These belief functions can be of great interest to represent a reality as faithfully as possible, for example to perform a partial classification [8], i.e. decisions in favor of a group of classes.

The main idea of this thesis is to develop such deep evidential networks in more depth by exploiting methods developed at LGI2A allowing one to consider finer knowledge about the quality [12, 14] and the dependence of information [11], or the ignorance in predictions [9,10].

Two applications are envisaged: Image analysis from drones and fish population analysis.

To apply, please send the following documents grouped in one pdf file: your CV, your grades for the current and past years, a motivation letter, and at most two recommendations (optional) to sebastien.ramel@univ-artois.fr, frederic.pichon@univ-artois.fr and david.mercier@univ-artois.fr

References

[1] C. M. Bishop. Pattern recognition and machine learning, 5th Edition. Information science and statistics. Springer, 2007.
[2] T. Denoeux. A neural network classifier based on dempster-shafer theory. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 30(2):131–150, 2000.
[3] T. Denœux. Calibrated model-based evidential clustering using bootstrapping. Information Science, 528:17–45, 2020.
[4] I. Goodfellow, Y. Bengio and A. Courville: Deep Learning (Adaptive Computation and Machine Learning), MIT Press, Cambridge (USA), 2016.
[5] L. Huang, T. Denoeux, P. Vera, and S. Ruan. Evidence fusion with contextual discounting for multi-modality medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 401–411. Springer, 2022.
[6] L. Huang, S. Ruan, P. Decazes, and T. Denoeux. Lymphoma segmentation from 3D PET-CT images using a deep evidential network. International Journal of Approximate Reasoning, Volume 149, pages 39-60, 2022.
[7] F. Li, S. Li, and T. Denœux. Combining clusterings in the belief function framework. Array, 6:100018, 2020.
[8] L. Ma and T. Denœux. Partial classification in the belief function framework. Knowledge-Based Systems, 214: article 106742, 2021.
[9] P. Minary, F. Pichon, D. Mercier, E. Lefèvre and B. Droit. Evidential joint calibration of binary SVM classifiers, Soft Computing, pp 4655-4671, Vol. 23, No. 13, 2019.
[10] S. Ramel, F. Pichon and F. Delmotte. A reliable version of choquistic regression based on evidence theory, Knowledge-Based Systems, KBS, pp 106252, Vol. 205, 2020.
[11] F. Pichon. Canonical decomposition of belief functions based on Teugels’ representation of the multivariate Bernoulli distribution. Information Sciences, 428:76-104, 2018.
[12] F. Pichon, D. Dubois, and T. Denœux. Relevance and truthfulness in information correction and fusion. International Journal Approximate Reasoning, 53(2):159–175, 2012.
[13] F. Pichon, D. Dubois, and T. Denoeux. Quality of information sources in information fusion. In Éloi Bossé and Galina L. Rogova, editors, Information Quality in Information Fusion and Decision Making, pages 31–49. Springer, 2019.
[14] F. Pichon, D. Mercier, E. Lefèvre, and F. Delmotte. Proposition and learning of some belief function contextual correction mechanisms. International Journal Approximate Reasoning, 72:4–42, 2016.
[15] G. Shafer. A mathematical theory of evidence, volume 42. Princeton university press, 1976.
[16] Z. Tong, P. Xu, and T. Denoeux. An evidential classifier based on dempster-shafer theory and deep learning. Neurocomputing, 450:275–293, 2021.
[17] Z. Tong, P. Xu, and T. Denœux. Fusion of evidential cnn classifiers for image classification. In International Conference on Belief Functions, pages 168–176. Springer, 2021.

Profil du candidat :
Master’s degree or equivalent in Computer Science or a related field

Formation et compétences requises :
Strong background in machine learning and deep learning

Experience with programming languages such as Python and TensorFlow / Keras

Excellent written and oral communication skills

Strong problem-solving and analytical skills

Adresse d’emploi :
LGI2A – Université d’Artois – Béthune – France – (https://www.lgi2a.univ-artois.fr).

Document attaché : 202303231217_Offre_These_2023_LGI2A_DLwithDST.pdf