The Machine Learning for Integrative Genomics team 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 : 5PhD students, 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/).
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.
Activities :
– 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
Required skills :
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.
The candidat must have a PhD in computer science, machine learning, or computational biology.
The position is available from March 1, 2025.
Application :