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

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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.

For a better knowledge of the proposed research subject :
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