Physically-informed Machine Learning for Modelling

Le descriptif de l’offre ci-dessous est en Anglais_

**Niveau de diplôme exigé **:Bac + 4 ou équivalent

**Fonction **:Stagiaire de la recherche

**A propos du centre ou de la direction fonctionnelle**:
The Inria center at Université Côte d'Azur includes 42 research teams and 9 support services. The center’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM...), but also with the regional economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

**Contexte et atouts du poste**:
**Mission confiée**:
Plants live in a constantly changing environment that happens to be unfavourable or even hostile. Therefore, plant defense against biotic threats requires multiple signaling processes responsible for surveillance, perception, and immune response activation that are influenced by varying spatial and temporal factors. On the top of that, those interactions are based on a molecular dialogue between the pathogen and its host that occur at different time frames, altogether concurring for a successful or unsuccessful infection.

The flourishing of omics techniques has led to the possibility of studying complex biological systems, through the analysis of its content at the molecular level. Transcriptomics is by far the most used omics providing the quantification of change in gene expression. Time-course transcriptomics data are analyzed considering each time point as independent and using approaches based on profile analysis in which the temporal continuity of the data is not fully appreciated. An alternative approach consists in the use of mathematical models including regression and spline models. However, these models generally fail to provide mechanistic interpretations. On the top of that, transcriptome analysis with high resolution in time is challenging, particularly with plant tissues, resulting in longitudinal experiments usually composed of few time points. Those are insufficient to robustly infer statistically significant changes over the infection.

Despite often longitudinal experiments are performed, current available analysis methods do not explicitly consider the time dependency of successive observations.

**Principales activités**:
Task 1. Identify possible mathematical models to describe typical omics time series and develop a suitable hybrid machine learning framework, based on both time series observations and modeling principles.

**Compétences**:
**Avantages**:

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training

**Rémunération**:
Traineeship grant depending on attendance hours.

**Informations générales**:

- **Thème/Domaine**: Optimisation, apprentissage et méthodes statistiques
Calcul Scientifique (BAP E)
- **Ville**: Sophia Antipolis
- **Centre Inria**: Centre Inria d'Université Côte d'Azur
- **Date de prise de fonction souhaitée**: 2025-02-01
- **Durée de contrat**: 5 mois
- **Date limite pour postuler**: 2025-01-31

**Consignes pour postuler**:
**Sécurité défense**:
Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

**Politique de recrutement**:
Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

**Contacts**:

- **Équipe Inria**: ACUMES
- **Recruteur**:
**L'essentiel pour réussir**:
With a scientific background, you are interested in combining artificial intelligence, biiology and scientific computing in a research environment.

**A propos d'Inria**:
Inria