Archives

Développement d’un outil d’analyse bibliométrique

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

Laboratoire/Entreprise : CNAM, CEDRIC
Durée : 3 mois
Contact : sle.contact@cnam.fr
Date limite de publication : 2023-09-10

Contexte :
Le travail s’effectuera au Centre d’Etudes et De Recherche en Informatique et Communications (CEDRIC) du Conservatoire Nationale des Arts et Métiers (CNAM). Les domaines d’expertise du laboratoire CEDRIC portent sur l’ingénierie des systèmes d’information et de décision, le datamining, des bases de données avancées, etc.

Sujet :
Dans le cadre d’un travail collaboratif avec l’Université Paris 1, il s’agit d’étudier le domaine des applications intelligentes. En effet, plusieurs applications ainsi que les appareils dits ‘intelligents’ apparaissent et se développent. Ce domaine étant en plein essor, il n’est pas encore structuré. Aucun travail n’existe qui énumère, classifie et organise les domaines concernés. L’objectif est de développer un outil d’analyse bibliométrique des métadonnées des publications scientifiques disponibles dans les bases telles que Scopus afin d’analyser la terminologie existante et d’établir les typologies des domaines et des appareils intelligents.

Pour postuler, merci d’envoyer votre candidature avant le 1 septembre 2023 à sle.contact@cnam.fr. La candidature doit inclure :
– Curriculum Vitae à jour,
– Lettre de motivation,
– Relevés des notes,
– Eventuellement une ou plusieurs lettres de recommandation.

Profil du candidat :
Capacités d’analyse, capacités rédactionnelles.

Formation et compétences requises :
Bac+3/Bac+5 en Informatique
Python souhaitable, Modélisation conceptuelle, Algorithmique.

Adresse d’emploi :
2, rue Conté, Paris 75003

Bioinformatician (M/F) – Description, Storage, and Standardization of Datasets and Workflows

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

Laboratoire/Entreprise : Institut Pasteur
Durée : 23 mois
Contact : herve.menager@pasteur.fr
Date limite de publication : 2023-10-31

Contexte :
The ShareFAIR project (PEPR “Digital Health”) aims to promote the sharing and exchange of health data and their analysis, with a focus on interoperability, reusability, and transparency.

Bioinformatic analyses are complex and rely on various tools that need to be configured and chained together. In this context, improving the reproducibility of the obtained results is of paramount importance, especially in the field of health. This is typically achieved through the design, implementation, and execution of workflows (e.g. Snakemake, Nextflow), which offer numerous advantages, such as improving the reproducibility of analyses and better tracking of data provenance.

These workflows are generally scattered across public repositories, poorly annotated, and difficult to query. Challenges, therefore, include the standardization and annotation of datasets and workflows, as well as their synthesis into interoperable, shareable, and reusable workflows.

More information here:

Bioinformatician (M/F) – Description, Storage, and Standardization of Datasets and Workflows

Sujet :
Within the scope of this project, we are seeking an engineer specialized in bioinformatics workflows, data, and knowledge engineering to contribute to the definition and implementation of standards and best practices to achieve these objectives. The successful candidate will work closely with a multidisciplinary team, including bioinformatics researchers and engineers, developers, and data management experts.

Main responsibilities:
– Identification of standards for the representation and annotation of workflows:
– Perform an in-depth analysis of existing standards such as RO-Crate, EDAM, and others that are relevant.
– Evaluate their applicability to the specific needs of the ShareFAIR project.
– Recommend and justify appropriate choices of standards for the representation and annotation of workflows.
– Construction of a knowledge base integrating the identified standards:
– Design and implement an infrastructure for the creation of a consolidated knowledge base, using the selected standards.
– Develop automated pipelines for the integration and management of data from different sources.
– Collaborate with the team to ensure the quality, consistency, and accuracy of data in the knowledge base.
– Adaptation and improvement of concepts borrowed from standards:
– Examine the scope and limitations (in terms of quality and coverage) of the identified standards.
– Propose improvements and adaptations to meet the specific needs of the ShareFAIR project.
– Implement these improvements in collaboration with the development team.
– Depending on the profile, assume the role of project manager

Profil du candidat :
Bachelor’s degree (Bac +5) in computer science or bioinformatics.

The Hub of Bioinformatics and Biostatistics and Institut Pasteur are committed to promoting gender equality, and female candidates are encouraged to apply.

Formation et compétences requises :
– Proficiency in Python and/or Java for software development.
– Solid knowledge of databases, including SQL and/or NoSQL.
– Familiarity with knowledge representation formats such as JSON and RDF.
– Understanding of ontologies and bioinformatics workflows (an advantage).
– Ability to work independently and collaborate effectively within a multidisciplinary team.
– Good communication and documentation skills.
– Proficiency in professional English.

Adresse d’emploi :
Institut Pasteur, Paris, France

Domain-specific software development in natural language

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

Laboratoire/Entreprise : IRISA (Rennes) / ALTEN (Rennes)
Durée : 3 ans
Contact : zoltan.miklos@irisa.fr
Date limite de publication : 2023-10-31

Contexte :
As a number domains and industries go through a digital transformation, one can observe a constant creation of demand for programmers. While these industries face a shortage of available software developers, the programming tasks are very specific : they require specific domain knowledge and only a modest level of programmings skills. Even this is an important phenomenon, and a basic programming skill would be desirable for the majority of professions of the 21st century, the public education curricula do not address this problem sufficiently. Certain industries face a shortage of available software developers and this problem is likely to increase.

A number tools 1 -often based of artificial intelligence- are available to address this problem and enable people with no or little programming skills to become productive developers. Recently, a number of AI-based tools -ChatGPT, Copilot, CodeClippy 2, etc. – emerged that enable to generate code in different programming languages, out of natural language. These tools could largely improve the productivity of software developers, but to make use of these tools, one still needs competences in programming languages and an understanding of the generated code.

This thesis aims to develop methodologies and tools that can enable or support do- main specialists to engage in activities that result executable software. Specifically, we envisage that they not only describe their programming tasks in natural language but they test, and debug their software in natural language, without interacting with the code itself. We would like to develop tools and methodologies to realise this vision in two different use cases.

Sujet :
We would like to develop a methodology to develop domain-specific applications in natural language. The methodology should include the following aspects :

Program synthesis: Generating code out of natural language descriptions to a specific target environment

Guiding the developer in the writing phase : We would like to develop methods to guide the developer to improve the provided textual description of the task if the provided text description is not sufficiently precise, to generate a code.
https://cacm.acm.org/news/263950-no-code-ai-platforms-and-tools/fulltext
https://github.com/CodedotAl/gpt-code-clippy/wiki

Guiding the developer in the testing/debugging phase: We will develop methodologies to correct the generated program, without specific coding skills. In particular, if the the developers discover some unexpected behavior in the executed code, they should be able to modify their description. For this, they also need guidance on how to change the original text. Potentially, they interact with a visual representation of the code rather than the original text, but they should be able to change the code to correct the behavior of their software.
We plan to develop methodologies and tools for two use cases: autonomous vehicles simulation software testing. In both of the scenarios, the goal is to develop simple software with low complexity that requires only basic programming skills, but specific domain knowledge.

Autonomous vehicles test scenarios
In this use case, we will focus on the use case of autonomous vehicles, where one needs to develop test scenarios for the driving licence of the autonomous vehicles. These sce- narios are described in a well-defined, standard language the OpenScenario 3 and Open- Road 4. These scenarios can be executed using a scenario execution software, that gene- rates a visual presentation of the defined scenario.

Software testing
In this use case we would like to develop methods and tools to support software testing. The goal is to obtain executable test scripts out of natural language descriptions of test scenarios. If the resulting test script does not correspond to the intended scenario, we user should have guidance and suggestions how to modify the text input describing the task to get the desired results.

Research questions
Program synthesis [8] is a research domain that aims to develop methods that can synthesize executable code out of high level descriptions and domain specific languages (DSLs). Researchers have proposed a variety of methods, including the use of satis- fiability or SMT solvers, reasoners, and also evolutionary computing. The most recent and advanced methods are based on the technique of neurosymbolic programming [4]. These techniques enable to combine symbolic methods to assure that the hard (and soft) constrains that correct synthesized software are satisfied, with (neural network-based) machine learning. Some important contributions in this area include [2], [5], [6], [13], [14], [16], [1] , [10]. Some of the neurosymbolic programming systems are available as open source projets, including Dreamcoder (Ellis et al. [7]).

Our planned work will use neurosymbolic techniques. While these methods enable to

realise powerful tools, they do not address several points that are very important in our context :

https://www.asam.net/standards/detail/openscenario/
https://github.com/The-OpenROAD-Project

Interaction. We would like that the user can interactively influence the generation process. While some papers propose interactive synthesis, such as [18], they assume that the developer understands the synthesized code, while we would like that the interaction is based on natural language. Phrases in natural language could have to much ambiguity to define programming tasks. When we would like to guide the programmer we might need to rely on a different representation. This could be for example a description of the scenario in a controlled language [12], or other representation that is easy to understand. We would like to avoid the developer has to read the code itself.
Guiding the expert in the programming phase can require a number of methods, including the identification of ambiguous parts of the programs. Other techniques could involve proposing auto-completion techniques. Auto-completion techniques are widely used in different areas such as in information retrieval [3], in (graph) databases [17]. We propose specific auto-completion mechanisms for this form of software development. In this context, auto-completion should take into account the specific constraints of the domain. In our work, we would like to enable developers to define certain domain knowledge in the form of constraints. We would like to exploit these constraints to generate the auto-completion options. Methods for generating auto-completion suggestions -in the presence of constraints- might in- include probabilistic reasoning [15] or machine learning-based techniques. Examples of the use of these techniques in other domains include [9] or [11].
Bibliographie
J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang,C. Cai, M. Terry, Q. Le, and C. Sutton. Program synthesis with large language models, 2021.
R. Bunel, M. J. Hausknecht, J. Devlin, R. Singh, and P. Kohli. Leveraging grammar and reinforcement learning for neural program synthesis. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30- May 3, 2018, Conference Track Proceedings. OpenReview.net, 2018.
F. Cai and M. de Rijke. A Survey of Query Auto Completion in Information Retrieval. Now Publishers Inc., Hanover, MA, USA, 2016.
S. Chaudhuri, K. Ellis, O. Polozov, R. Singh, A. Solar-Lezama, and Y. Yue. Neurosymbolic programming. Foundations and Trends® in Programming Languages, 7(3) :158–243, 2021.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton. A simple framework for contras- tive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning, ICML’20. JMLR.org, 2020.
M. D. Cranmer, A. Sanchez-Gonzalez, P. W. Battaglia, R. Xu, K. Cranmer, D. N. Spergel, and S. Ho. Discovering symbolic models from deep learning with inductive biases. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems 33 : Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
K. Ellis, C. Wong, M. I. Nye, M. Sablé-Meyer, L. Morales, L. B. Hewitt, L. Cary,A. Solar-Lezama, and J. B. Tenenbaum. Dreamcoder : bootstrapping inductive program synthesis with wake-sleep library learning. In S. N. Freund and E. Yahav, editors, PLDI ’21 : 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, Virtual Event, Canada, June 20-25, 2021, pages 835–850. ACM, 2021.
S. Gulwani, O. Polozov, and R. Singh. Program synthesis. Foundations and Trends® in Programming Languages, 4(1-2) :1–119, 2017.
N. Q. V. Hung, M. Weidlich, N. T. Tam, Z. Miklós, K. Aberer, A. Gal, and B. Stantic. Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models. Information Systems, 83 :166 – 180, 2019. http://www.sciencedirect. com/science/article/pii/S030643791830320X.
N. Jain, S. Vaidyanath, A. Iyer, N. Natarajan, S. Parthasarathy, S. Rajamani, and R. Sharma. Jigsaw : Large language models meet program synthesis. In Proceedings of the 44th International Conference on Software Engineering, ICSE ’22, page 1219–1231, New York, NY, USA, 2022. Association for Computing Machinery.
K. Kikuchi, E. Simo-Serra, M. Otani, and K. Yamaguchi. Constrained graphic layout generation via latent optimization. In Proceedings of the 29th ACM International Conference on Multimedia, MM ’21, page 88–96, New York, NY, USA, 2021. Association for Computing Machinery.
T. Kuhn. A Survey and Classification of Controlled Natural Languages. Computational Linguistics, 40(1) :121–170, 03 2014.
A. Murali, A. Sehgal, P. Krogmeier, and P. Madhusudan. Composing neural learning and symbolic reasoning with an application to visual discrimination. In L. D. Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pages 3358–3365. ij- cai.org, 2022.
E. Parisotto, A. Mohamed, R. Singh, L. Li, D. Zhou, and P. Kohli. Neuro-symbolic program synthesis. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. Open-Review.net, 2017.
J. Pearl. Probabilistic reasoning in intelligent systems : networks of plausible inference. Morgan Kaufmann, San Francisco, Calif., 2009.
R. Shin, M. Allamanis, M. Brockschmidt, and O. Polozov. Program Synthesis and Semantic Parsing with Learned Code Idioms. Curran Associates Inc., Red Hook, NY, USA, 2019.
P. Yi, B. Choi, S. S. Bhowmick, and J. Xu. Autog : A visual query autocompletion framework for graph databases. Proc. VLDB Endow., 9(13) :1505–1508, sep 2016.
T. Zhang, L. Lowmanstone, X. Wang, and E. L. Glassman. Interactive program synthesis by augmented examples. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, UIST ’20, page 627–648, New York, NY, USA, 2020. Association for Computing Machinery.

Profil du candidat :
very motivated, scientific curiosity, familiarity with NLP, machine learning

Formation et compétences requises :
titulaire d’un Master en Informatique (ou euivalent), très bon niveau français et anlais

Adresse d’emploi :
Univ Rennes CNRS IRISA
Campus universitaire de Beaulieu
263 Avenue du General Leclerc – Bat 12 (D267)
F-35042 Rennes Cedex
France

et

ALTEN
12 Rue du Patis Tatelin, 35000 Rennes

Document attaché : 202307201009_these_cifre_ALTEN_v2.pdf

Ingénieur de recherche – déploiement outils IA sur Cluster (H/F)

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

Laboratoire/Entreprise : CRIStAL – UMR9189
Durée : 24 mois
Contact : clarisse.dhaenens@univ-lille.fr
Date limite de publication : 2023-07-21

Contexte :
En relation avec les ingénieurs du CRI, cet ingénieur aura pour mission de démocratiser l’utilisation du cluster Big Data acquis dans le cadre du CPER Data.
Pour cela, il :
– rencontrera des scientifiques non spécialistes de l’IA pour en comprendre leurs besoins
– procédera à la veille technologique sur les bibliothèques logicielles utilisées
– déploiera des outils d’IA (dans un premier temps issus des outils disponibles, dans un 2e temps, issus du WP1)
– mettra à disposition un environnement de programmation interactif connecté à une partie de l’infrastructure.

Sujet :
Déploiement d’outils d’IA sur clusters de calculs pour utilisation par non spécialistes.
2 étapes :
1. pré-identification de projets collaboratifs faisant intervenir des chercheurs en sciences du numérique et des chercheurs d’autres disciplines.
2. mise à disposition d’outils d’IA pour les non-spécialistes

https://emploi.cnrs.fr/Offres/CDD/UMR9189-CLADHA-002/Default.aspx

Profil du candidat :
Connaissance avancée en développement logiciel (langages C, C++, Python, Javascript)
Connaissance en intelligence artificielle, et notamment dans l’utilisation d’outils.
Connaissance en programmation distribuée, multi-coeurs….

Formation et compétences requises :
Niveau 7 – (Bac+5 et plus)

Adresse d’emploi :
VILLENEUVE D’ASCQ

CFP: Workshop on Artificial Intelligence for Predictive Maintenance and IIoT (AI4PMI) @IEEE AICCSA Egypt 4-7 Dec. 2023

Date : 2023-12-04 => 2023-12-07
Lieu : Le Caire, Egypte

[apologies if you receive multiple copies of this CFP]

Dear Colleagues,

Please find below a call for papers for Workshop on Artificial Intelligence for Predictive Maintenance and IIoT (AI4PMI) which will be co-located with the IEEE AICCSA 2023 conference, which will be held in the National Telecommunication Institute – Smart Village, Giza, Egypt, 4 – 7 December, 2023.

https://aiccsa-wsai4pmi1.gitlab.io/website/

Deadline for submissions: July 15, 2023 (AoE)

We are facing the 4th Industrial Revolution revolving around IoT, Edge Device and Machine Learning applications. While IoT is now part of our daily environment, these paradigms, combined together, open the door to a handful of new possibilities for predictive maintenance. They make this possible by enabling the Edge to “talk” and send real-time data.

Since predictive maintenance is aimed at finding the right balance between scheduled maintenance and curative maintenance, it requires the use of machine learning (ML) based solutions to explore and exploit the data generated. Innovative solutions are required which go beyond the current predictive maintenance systems by exploiting Artificial Intelligence techniques. This need to go beyond can be seen in the case of supervision systems where every new failure risk may not be predictable beforehand but with the use of machine learning, the decision process can be made more reactive to failures and more robust against attacks.

As this research area is still new, many scientific barriers need to be overcome and different challenges need to be addressed ranging from the data acquisition to the type of machine learning solution applied. Therefore, this workshop aims to bring together researchers, practitioners, and industry experts to discuss and explore the latest developments, methodologies, and applications of Artificial Intelligence techniques in predictive maintenance and IIoT. The primary goals of the workshop are to foster collaboration, exchange ideas, and promote advancements in this rapidly evolving field.

Topics include but not limited to:

– Data: acquisition & preprocessing, sensor fusion & data integration, benchmarks & datasets, simulations & digital twins
– Features: extraction, selection
– Targets: anomaly detection, fault diagnosis, fault prediction, root cause analysis, recovery protocols design, data privacy protection, knowledge capitalization
– Methods: deep learning, generative methods, explainability & interpretability, transfer learning, domain adaptation, real time algorithms, optimization, evolutionary algorithms, open-world machine learning, continual learning, symbolic AI, graph-based architectures (knowledge graphs, Graph neural networks, …)
– Edge computing: tiny ML, distributed architectures (federated learning, distributed learning, multi-agent system

These topics provide a comprehensive coverage of the technical challenges and advancements in machine learning for predictive maintenance and IIoT. They offer opportunities for researchers and practitioners to discuss their work, share insights, and collaborate on solving real-world maintenance problems.

Submission procedure

Please see this page for the submission instructions.
https://aiccsa-wsai4pmi1.gitlab.io/website/

Organizing Committee :

– Guillaume MULLER, École des Mines de Saint-Étienne, France
– Anaïs Lavorel, Université Claude Bernard Lyon 1, France
– Kamal Singh, Université de Saint-Étienne, France

Lien direct

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

Offre de thèse dans le cadre du projet MOCKUP : Meteorological Observation ontologies and Contextual Knowledge for final User Policies

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

Laboratoire/Entreprise : IRIT/CRNM
Durée : 3 ans
Contact : cassia.trojahn@irit.fr
Date limite de publication : 2023-07-01

Contexte :
Le développement durable est inscrit profondément dans les politiques publiques. L’une des conséquences en est la recherche d’outils techniques ou scientifiques pour réaliser cet objectif, et c’est ce que l’on appelle la science de la durabilité. Ce nouveau champ de la science est hautement interdisciplinaire avec une interaction entre les sciences de la société et de l’humain, et les sciences de la nature ou les sciences formelles. Dans ce contexte, les actions interdisciplinaires en sciences des données sur le pilotage de l’aménagement des territoires méritent d’être développées. Ce pilotage est en effet guidé par des données issues d’une variété de domaines (géographie, économie, environnement, etc.). Les points de vue sur ces données sont contextuels et évolutifs.

Le projet MOCKUP est donc centré sur l’observation et le pilotage du territoire, en s’appuyant sur une représentation sémantique des données entrantes et des points de vue sur ces données. Ses objectifs sont les suivants :
• l’apprentissage de points de vue selon l’usage des données, en particulier des données environnementales ;
• la représentation de points de vues pour définir des ontologies dynamiques et adaptables aux usages et contexte ;
• le raisonnement contextuel sur les données pour l’aide à la décision.

Sujet :
Dans ce projet, notre hypothèse est de considérer que l’appropriation de données décrites par une ontologie, dite de référence, passe par la prise en compte des usagers (et des usages), tant des publics ciblés que des contextes d’utilisation, dans la manière de présenter cette ontologie. Nous proposons pour cela de définir la notion de point de vue, considéré comme un prisme, une manière de présenter l’ontologie en partie ou en totalité, de manière adaptée à des utilisateurs et à un contexte d’usage.

Pour s’adapter au contexte d’usage, nous voulons donner un caractère dynamique, adaptatif et contextuel à l’ontologie, ce qui est négligé dans les travaux sur la construction des ontologies dans l’état de l’art. Bien que l’évolution des ontologies, pour leur donner un minimum de caractère “dynamique”, ait fait l’objet de nombreuses recherches dans la communauté Web sémantique, la problématique traitée ici est différente et nouvelle : l’ontologie serait stable et suffisamment riche pour être adaptée par de nouvelles instantiations donnant lieu à différentes vues sur celle-ci, selon les usages et les contextes.

Il s’agira de reformuler, simplifier ou extraire des sous-ensembles de l’ontologie, et si besoin d’envisager des présentations adaptées, et cela de façon adéquate aux contextes d’usage. Ces ontologies adaptatives peuvent donc être le pilier pour le raisonnement dépendant du contexte d’usage.

Profil du candidat :
Master en informatique (si possible avec mention), formé à la représentation de connaissances et aux technologies du web sémantique. Compétences en programmation, bonnes capacités de rédaction, y compris en anglais.

Formation et compétences requises :
Master en informatique (si possible avec mention), formé à la représentation de connaissances et aux technologies du web sémantique. Compétences en programmation, bonnes capacités de rédaction, y compris en anglais.

Adresse d’emploi :
Le la doctorant.e bénéficiera d’une allocation doctorale interdisciplinaire (ADI) cofinancée par l’Université de Toulouse et la région Occitanie (démarrage octobre 2023 pour 3 ans). La thèse sera co-encadré par Cassia Trojahn (IRIT, UT2J), Christophe Baehr (CRNM) et Nathalie Aussenac-Gilles (CNRS/IRIT).

Postdoctoral position: Machine learning for time series prediction in environmental sciences

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

Laboratoire/Entreprise : LIFAT (EA 6300), Université de Tours
Durée : 18 months
Contact : nicolas.ragot@univ-tours.fr
Date limite de publication : 2023-09-30

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 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.
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.
The project actors are: BRGM, Université d’Orléans, Université de Tours, CNRS, INRAE, and ATOS and ANTEA companies.

Sujet :
While the BRGM will have in charge to collect and arrange data (ground waters levels at different locations) and to benchmark predictions with mechanistic models as well as with classical prediction AI tools, the goal of the postdoc will be to build new prediction models able to integrate several sources of information like:
– meteorological data
– spatial information, i.e. geolocalization of sensors and locations of predictions to be made; topological information such as altitude
– integration of knowledge from mechanistic models as well as from expert knowledge (impact of attributes and variables used)
– etc.
The scientific locks are clearly related to:
– multivariate time series
– short-term to long term predictions (horizon)
– going from local predictors to ‘connected predictors’, i.e. how to use information coming from sensors spread over the area of study
And if possible:
– considering heterogenous data (time series, climatic data, topological information, combination with other models…)
– having an idea of how continuous learning (work of a PhD) could be done on such models.
Studying transformers and Spatio-Temporal Graph Neural Networks will be particularly investigated.
Of course, models will have to be implemented, learnt and compared with classical models on benchmarks.

Profil du candidat :
The candidate should be experimented with machine learning (Python) and time series.

Formation et compétences requises :
PhD in machine learning (computer sciences or applied mathematics)
Skills:
– a strong experience in data analysis and machine learning (theory and practice of deep learning in python) is required
– experiences/knowledge in time series prediction and environmental science is welcome
– curiosity and ability to communicate (in English at least) and work in collaboration with scientists from other fields
– ability to propose and validate new solutions and to publish the results
– autonomy and good organization skills

Adresse d’emploi :
Affiliation: Computer Science Lab of Université de Tours (LIFAT), Pattern Recognition and Image Analysis Group (RFAI)

Document attaché : 202307171132_Fiche de poste Pdoc Junon.pdf

CDD Ingenieur

Offre en lien avec l’Action/le Réseau : FedSed/Innovation

Laboratoire/Entreprise : EPIONE Inria
Durée : 24 mois
Contact : marco.lorenzi@inria.fr
Date limite de publication : 2023-09-30

Contexte :
As a research engineer, you will be attached to the Department of Experimentation and Development (SED) and to EPIONE Research Group. You will integrate the personnel staff for engineering innovation and Inria technology transfer and work on the development of the project Fed-BioMed. This project is the result of the synergy between research and engineering teams for the development of an open framework for federated learning in healthcare. The project is in collaboration with an international network of clinical collaborators and hospitals, that will provide clinical use cases for the translation of the development work into the real world. The project is based on Agile development methods, and is supported by the transfer, innovation and partnerships service (STIP) of the center, responsible for prospecting and helping the team in the implementation of research contracts and technology transfer.

At the end of this experience, you will have consolidated a broad range skills in software engineering with application to a unique scientific context, through the deployment of top-notch Data Science paradigm. This experience will allow you to consider careers as an engineer in research and development in national organizations ( Inria, INRAE, CNRS, CEA), industrial research centers, SMEs and digital start-ups.

Sujet :
The engineer will be mainly involved in the software developments within the framework of the project Fed-BioMed. She / He will also participate in the design and development of highly innovative scientific software platforms for scientific research and experimentation activities.
The engineer will also work on the co-development of prototype software integrating the technological core ideas proposed by the researchers. The engineer will work within a software development team using agile methodologies (mixed Scrum-Extreme Programming method), in a context where she / he will be able to constantly improve her / his skills.

Profil du candidat :
This position is intended for PhD, Post-docs or Engineers in the field of computer sciences (IT, image processing, robotics, bioinformatics, automation, simulation and high-performance computing), with a demonstrable background in machine learning and AI-related topics.

• Software development / software engineering.
• Solid experience in software development.
• Experience in project development, preferably using Agile methodology.
• Experience in Linux development
• Training and / or experience in one or more of the following fields: Data science / Statistics, Machine Learning, Artificial Intelligence, Optimization,
• ML libraries (e.g. Pytorch, TensorFlow, Keras, Julia),
• Medical data analysis,
• Database management;
• Knowledge / experience in an R&D environment (public or private sector).

Formation et compétences requises :
Skills / know-how:
• Programming languages: Python, C, C ++
• Experience with Machine Learning libraries (e.g PyTorch, TensorFlow, Keras, Julia)
• Know how to implement the methods and tools underlying the compilation, version control, continuous integration and development through testing in a context of agile methods
• Knowledge of agile methodology
• Good writing and communication skills
• Good level of technical and scientific English, both oral and written.
• Knowledge in one or more of the following tools is also a plus:
o version management, continuous integration, packaging and deployment (git, jenkins, cpack, conda, docker)
o graphical interfaces: Qt, Electron, Gtk, …

Benefits

• Subsidized canteen,
• public transport partially reimbursed
• Leave: 7 weeks of annual leave + 10 days of RTT (full-time basis) + possibility exceptional absence authorizations (e.g. parental leave, moving)
• Possibility of teleworking (after 6 months of seniority) and working time arrangement
• Professional equipment available (videoconferencing, loan of equipment, computers, etc.)
• Social, cultural and sports benefits (Association for the management of social works Inria)
• Access to vocational training
• Social Security

Salary

Depending to experience

Adresse d’emploi :
Inria Sophia Antipolis
2004 Route des Lucioles,
06902 Sophia Antipolis

Offre de thèse interdisciplinaire IRIT/CLLE (Toulouse)

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

Laboratoire/Entreprise : IRIT/CLLE (Toulouse)
Durée : 3 ans
Contact : cassia.trojahn@irit.fr
Date limite de publication : 2023-09-30

Contexte :
Le projet RIMO cherche à construire le premier référentiel FAIR, conceptuel, terminologique et interdisciplinaire de la mémoire. Consultable au sein d’une plateforme en libre accès, il autorisera différents niveaux d’abstraction et d’interrogation. L’utilisateur (grand public, praticien ou spécialiste des sciences de la mémoire) pourra, en fonction de son expertise, exploiter les concepts de la mémoire en prenant différents points de vue. Ces points de vue pourront être des acceptations générales de ce que l’on entend par “mémoire” autant que des théories et outils spécifiques.

Sujet :
Les objectifs sont de :
(1) constituer un corpus textuel scientifique annoté à partir de textes issus des sous-disciplines de la science de la mémoire intégrées au champ d’application du projet. Ceci en s’appuyant sur un réseau de recherche (le GDR/Réseau Thématique Mémoire) et en exploitant une théorie cognitive des processus d’annotation ;
(2) exploiter, sur des corpus interdisciplinaires, des avancées récentes de l’intelligence artificielle dont l’apprentissage automatique et l’apprentissage par représentation afin d’extraire des termes et des relations entre eux. Les techniques du traitement du langage naturel, de l’extraction de connaissances utilisant des approches neuro-symboliques seront également mobilisées ;
(3) construire un modèle conceptuel du domaine de la mémoire au sein d’une ontologie qui doit tenir compte de différents points de vue et niveaux d’abstraction.

Profil du candidat :
Le projet de thèse proposé s’adresse principalement à un.e titulaire d’un Master en informatique, linguistique computationnelle, intéressé.e par les thématiques développées par l’IRIT et souhaitant construire une expertise à ce sujet (voir plus haut), tout en incorporant à son travail une expertise en psychologie sur les modèles à deux processus de mémoire (recollection, familiarité) et les représentations associées (représentation détaillée, représentation thématique).

Le projet proposé pourrait également convenir à un.e titulaire d’un Master en psychologie, déjà intéressé.e par les outils permettant d’étudier les processus de récupération en mémoire et les représentations associées, qui souhaiterait développer également des compétences en informatique (extraction de connaissances à partir de textes, construction d’ontologies, apprentissage automatique).

Formation et compétences requises :
(Voir profil)

Adresse d’emploi :
Toulouse

Offre de de thèse en co-tutelle (Perth – Australie à distribuer) – Attention date de dépôt des dossiers très proche

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

Laboratoire/Entreprise : University of Murdoch (Australia) – Tetis (Montpel
Durée : 36
Contact : maguelonne.teisseire@teledetection.fr
Date limite de publication : 2023-09-30

Contexte :
Joint PhD position
Data-Driven Methods for Modeling the 3D Structure of Plants

Sujet :
The aim of this PhD thesis is to develop data-driven techniques for modelling the 3D structure of plants and analyze how plant structure is affected by various intrinsic and extrinsic factors such as soil conditions and environmental factors. This is an important problem that has a wide range of applications in plant biology and agriculture. One of the main scientific challenges is to develop efficient algorithms for the extraction of features and patterns from 3D point clouds representing plant shape. Another challenge is to develop models that can simulate the growth and development of plant structures over time, taking into account various environmental factors. Another scientific question addressed in this project is how to analyze the complex relationships between plant structure and function at different scales. This involves the development of methods to measure and quantify plant traits such as biomass, leaf area, and stomatal density, and to relate these traits to plant function and performance. Overall, the project aims to advance our understanding of the structure-function relationships in plants and to provide new tools for plant breeders, ecologists, and agronomists to improve crop productivity and resilience in the face of environmental challenges.
Keywords: Deep Learning, 3D computer vision, shape analysis, geometric modelling.

Profil du candidat :
Qualification: The successful candidate is expected to have a MSc degree (or equivalent), with a significant research component, completed by September 2023, with background in either image processing, computer vision, computer graphics, machine learning applied for vision, or 3D geometry processing. Students with background in mathematics, especially 3D geometry, are highly encouraged to apply.

Formation et compétences requises :

Experience: The ideal candidate should have some knowledge and experience in at least one of the fields listed above. The successful candidate should have strong programming skills.

As for generic competences, we seek a qualified self-motivated professional, open to multidisciplinary, with capacity to undertake independent research, ability to work in a teamwork, and self-motivated.

Language Skills: Fluent written and verbal communication skills in English are required.

Adresse d’emploi :
The candidate should also be willing to spend 18 months in Australia and 18 months in France.

Document attaché : 202307101258_TETIS_Murdoch_Joint_PhD_position_2023.docx – Google Docs.pdf