Engineer - Infering Logical Abstractions of Reaction Networks By Neural Networks: Preparing The Data (M/F)

Le descriptif de l’offre ci-dessous est en Anglais_ **Type de contrat**: CDD **Niveau de diplôme exigé**: Bac + 5 ou équivalent **Fonction**: Ingénieur scientifique contractuel **A propos du centre ou de la direction fonctionnelle**: Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 15 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region. For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT). **Contexte et atouts du poste**: **Scientific Context**: Biological systems are frequently modeled using chemical reaction networks, where their temporal behavior is typically represented through sets of ordinary differential equations (ODEs). However, this approach assumes complete knowledge of reaction kinetics and numerical parameters, which is rarely achievable due to the limitations of wet-lab experimental techniques. To address this, alternative modeling strategies, such as Boolean networks and abstract chemical reaction networks, have been proposed for modeling biological system dynamics when only partial kinetic information is available. **Problem**: Model design in these discrete frameworks is often carried out manually because the statistical noise present in wet-lab data complicates automatic inference. While neural networks can outperform traditional approaches, they are often regarded as "black boxes" due to their lack of interpretability, making it difficult to understand the biological behavior they learn. **Aim**: The goal is to prepare the development and implementation of neural networks that are particularly suited to the inference of biological system dynamics. The long term aim is to identify architectures that are well-aligned with discrete models of biological systems, such as chemical reaction networks, while still leveraging the strengths of machine learning for processing complex, noisy datasets. **Mission confiée**: **Assignments**: In this engineering work, we start to prepare the learning dataset from the North Sea, which includes environmental variables and the abundance of 270 phytoplankton species. The state of the art on neural networks has to be studied. **Collaboration**: The work subscribes to the ANR project REBON. **Principales activités**: Prepare the research topic on Infering Logical Abstractions of Reaction Networks by Neural Networks **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 - Social security coverage **Informations générales**: - **Thème/Domaine**: Biologie numérique Statistiques (Big data) (BAP E) - **Ville**: Villeneuve d'Ascq - **Centre Inria**: Centre Inria de l'Université de Lille - **Date de prise de fonction souhaitée**: 2025-04-01 - **Durée de contrat**: 3 mois - **Date limite pour postuler**: 2025-04-06 **Consignes pour postuler**: Please send us your CV and cover letter **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**: LINKS - **Recruteur**: **L'essentiel pour réussir**: A general interest in the research field **A propos d'Inria**: Inria est l’institut national de recherche dédié aux sciences et technologies du numérique. Il emploie