Computational Biologist

**Date de parution**:11 févr. 2025

**Ville**:GIF-SUR-YVETTE

**Pays/Région**:FR

**Type de contrat**:CDI

**N° offre**:8034

Computational Biologist - Dynamic Systems Modeling H/F)
- Nous sommes un groupe pharmaceutique à dimension humaine, international et indépendant, gouverné par une Fondation. Notre modèle, singulier, fait notre fierté mais, surtout, nous permet de servir pleinement notre vocation : « engagés pour le progrès thérapeutique au bénéfice des patients ». Aujourd’hui leader mondial en cardiologie, nous avons choisi de devenir un acteur focalisé et innovant en oncologie d’ici 2030, en ciblant des cancers difficiles à traiter et en y consacrant plus de 70 % de notre budget R&D. Un défi que nous poursuivons en parallèle du développement de notre activité générique pour un accès à des soins de qualité pour tous, et à moindre coût.

Venez vivre et contribuez à faire vivre notre engagement #MovedByYou

**Role Description**
- The associated activities will be carried out in collaboration with a multidisciplinary team including therapeutic project managers, biologists, chemists, computational chemists, bioinformaticians and data engineers.

**Background**
- PhD in Computational Biology, Computational Physics, Bioinformatics, Computational Biophysics or Computational Mathematics applied to Biology, or a related field.

**Required Qualifications**
- Mathematical & Modeling Expertise:

- Solid foundation in ODE modeling, parameter inference, and statistical validation.
- Experience with stochastic and mechanistic modeling frameworks.
- Very strong skills in Python (including PySB, NumPy, SciPy, PyTorch/TensorFlow, Pandas, Matplotlib).
- Strong knowledge of differential equations systems applied to biology, chemistry or pharmacokinetics/pharmacodynamics.
- Understanding molecular biology, biochemistry, or pharmacology.
- Familiarity with high-throughput experimental data (e.g., transcriptomics, proteomics).
- Good knowledge of biological databases (examples: Chembl, Encode, GEO).
- Good knowledge of data analysis and machine learning techniques (using Python and Scikit Learn for clustering, dimensionality reduction, data preprocessing and statistics).
- Hands-on expertise in Physics-Informed Neural Networks (PINNs) and integration with experimental datasets is a strong plus.

**Preferred Skills**
- Experience with machine learning and AI techniques applied to biological data.
- Experience with knowledge graphs & graph theory.
- Knowledge of pharmacokinetics/pharmacodynamics (PK/PD) modeling.
- Familiarity with cloud computing platforms and tools for scalable data analysis.
- Hands-on experience with bioinformatics tools and databases.

**Soft skills**
- Demonstrated ability to work collaboratively in multidisciplinary teams and communicate complex scientific concepts effectively.
- Strong ability to work in a team is mandatory
- Pragmatic and result oriented
- Ability to work on multiple projects in parallel respecting deadlines
- Strong independence, strong organization skills
- Innovative and curious mind

**Language**English fluent