PhD (M/F) in development of machine learning methods for single-cell omics data integration

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 PhD (M/F), to be recruited in the context of the ERC StG MULTI-viewCELL, will be working on the development of a new method combining machine learning and biophysical modelling to model embryo development from spatial transcriptomics data.

Activities :

– design of a new mathematical method
– monitoring and study of publications relevant to the field
– programming/coding in Python (Pytorch)
– presentation of results at conferences
– interaction with team members and international collaborators

Required skills :

Degree : Master of Physics, data science, computer science, applied mathematics

We expect a candidate with a strong background in physics and/or machine learning. The candidate must also be proficient in high-level languages like Python. Familiarity with single-cell data 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.

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 9 people : 4PhD students, 3 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. The team recently won ERC StG funding, which is the subject of this recruitment.

Information about the project :

Application : Portail Emploi CNRS – Job offer