Informations générales
Intitulé de l'offre : Post doc (M/F) in development of machine learning methods for single-cell omics data integration (H/F)
Référence : UMR3738-DEBPHI-003
Nombre de Postes : 1
Lieu de travail : PARIS 15
Date de publication : mardi 17 juin 2025
Type de contrat : Chercheur en contrat CDD
Durée du contrat : 8 mois
Date d'embauche prévue : 1 novembre 2025
Quotité de travail : Complet
Rémunération : 3 081€ and 4 756€ gross monthly based on experience
Niveau d'études souhaité : Doctorat
Expérience souhaitée : Indifférent
Section(s) CN : 51 - Modélisation mathématique, informatique et physique pour les sciences du vivant
Missions
Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating exciting opportunities for machine learning to address outstanding biological questions. The postdoc, to be recruited will be working on the development of a new machine learning method allowing the inference of molecular mechanisms from the integration of spatial transcriptomics and single-cell multi-omics data.
Activités
- design of a new mathematical method
- monitoring and study of publications relevant to the field
- efficient programming/coding in Python (Pytorch)
- presentation of results at conferences
- interaction with team members and international collaborators
Compétences
Degree : PhD in computer science, machine learning, or computational biology
We expect a candidate with a strong background in machine learning or statistics. The candidate must also be proficient in high-level languages like Python. Familiarity with single-cell date and experience with existing single-cell methods and software would represent a strong advantage. Excellent communication skills and team spirit, and an ability to work in autonomy are essential. Fluent English both spoken and written is required.
Contexte de travail
The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed by the team: https://github.com/cantinilab). The team is composed of 7 people : 4PhD students, 1 post doc, 1 research engineer and 1 assistant. The team is associated with the Institut Pasteur's Computational Biology Department, UMR3738 and the PRAIRIE Artificial Intelligence Institute. This project will be funded by the PRAIRIE Institute (https://prairie-institute.fr/).
Contraintes et risques
Work on computer