Hybrid Mechanistic & AI Modeling of Microbiomes

SFBI; Jouy-en-Josas; Presencial; EUR 42 000 - 62 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Annual leave

Social assistance and loans

Holiday vouchers

Sport and cultural activities

Collective catering

Resumo da oferta

INRAE invites applications for a postdoctoral researcher to advance hybrid mechanistic–ML modeling of microbiomes. You will build a multi-scale framework linking GEMs and microbial community dynamics to predictive fermentation outcomes.

The role collaborates with MaIAGE, PROSE, and MICALIS teams, using PyTorch/JAX, Python, and COBRApy, and requires a PhD in systems biology or applied mathematics; English proficiency is required.

Qualificações

• PhD in systems biology, computational biology, bioinformatics, applied mathematics, biophysics, or equivalent.
• Strong mathematical/machine-learning skills: dynamical systems (ODEs, numerical simulation) and deep learning.
• Proficient Python development; ability to work in a multidisciplinary team and in English.

Responsabilidades

• Build the computational backbone of a multi-scale modeling framework linking individual strain metabolism to community dynamics.
• Run static/dynamic flux balance analyses across thousands of simulated microbial consortia.
• Integrate metabolic descriptors into a hybrid mechanistic–ML model constrained by biological equations.

Conhecimentos

Dynamical systems

Deep learning

Python development

Machine learning

Formação académica

PhD in systems biology/computational biology/bioinformatics/applied mathematics

Ferramentas

PyTorch

JAX

COBRApy

Descrição da oferta de emprego

Hybrid Mechanistic & AI Modeling of Microbiomes

microbiome metabolic modeling ODEs PINNS hybrid deep-learning model flux balance analyses

Description

INRAE presentation

The French National Research Institute for Agriculture, Food, and Environment (INRAE) is a major player in research and innovation. It is a community of 12,000 people with 272 research, experimental research, and support units located in 18 regional centres throughout France. Internationally, INRAE is among the top research organisations in the agricultural and food sciences, plant and animal sciences, as well as in ecology and environmental science. It is the world’s leading research organisation specialising in agriculture, food and the environment. INRAE’s goal is to be a key player in the transitions necessary to address major global challenges. Faced with a growing world population, climate change, resource scarcity, and declining biodiversity, the Institute has a major role to play in building solutions and supporting the necessary acceleration of agricultural, food and environmental transitions.

Work environment, missions and activities

You will build the computational backbone of a multi-scale modeling framework that links how individual bacterial strains metabolize plant matrices up to how whole microbial communities behave, so we can predict and steer which combinations of strains keep a targeted strain alive and active through fermentation.

Concretely, you will:
- Construct and curate genome-scale metabolic models (GEMs) for food-fermentation strains, using deep-learning-based reconstruction (e.g., DNNGIOR) and curated metabolic modules;
- Run static and dynamic flux balance analyses across thousands of simulated microbial consortia to prioritize strain combinations that support a targeted strain;
- Embed the resulting metabolic descriptors into a hybrid deep-learning model (compositional Neural ODEs) constrained by consumer–resource ecological equations;
- Combine scarce high-resolution experimental data with large public datasets and drive training with synthetic data from model forward simulations;
- Parameterize dynamic models with Physics-Informed Neural Networks (PINNs) to enforce biophysical constraints;
- Integrate an open multi-omics dataset with a validated predictive model to feed the project’s digital-twin fermentation platform.

You will work at the interface of simulation and experiment, interacting closely with the wet-lab and digital-twin teams, with access to the MIGALE and Jean Zay computing clusters.

Training and skills:
- PhD in systems biology, computational biology, bioinformatics, applied mathematics, biophysics, or equivalent;
- Strong mathematical/machine-learning skills: dynamical systems (ODEs, numerical simulation), deep learning (PyTorch/JAX or similar);
- Proficient Python development;
- Comfortable working in a multidisciplinary team and in English (French appreciated but not required).

Nice to have (any of):
- Genome-scale metabolic modeling / flux balance analysis (COBRApy or similar);
- Neural ODEs, PINNs, or hybrid mechanistic–ML models;
- Statistical analysis of large or genomic-scale datasets;
- Experience with microbial community or fermentation data.

You will work at the interface of simulation and experiment, interacting closely with the wet-lab and digital-twin teams, with access to the MIGALE and Jean Zay computing clusters.

You will be jointly hosted across MaIAGE (mathematical biology, PINNs, digital-twin design with Dr. Lorenzo Sala and Dr. Guillaume Gautreau) and PROSE (metabolic modeling of microbial ecosystems with Dr. Daniel-Rios Garza), embedded in a strong consortium with MICALIS gut-microbiome and fermentation expertise (Dr Julien Tap).

By joining our teams, you benefit from (depending on the type of contract and its duration):

• up to 30 days of annual leave + 15 days “Reduction of Working Time” (for a full time);
• social support: advice and listening, social assistance and loans;
• holiday and leisure services: holiday vouchers, accommodation at preferential rates;
• sports and cultural activities;
• collective catering.

You will be jointly hosted across MaIAGE (mathematical biology, PINNs, digital-twin design with Dr. Lorenzo Sala and Dr. Guillaume Gautreau) and PROSE (metabolic modeling of microbial ecosystems with Dr. Daniel-Rios Garza), embedded in a strong consortium with MICALIS gut-microbiome and fermentation expertise (Dr Julien Tap).