Postdoct Position in the Development of Machine Learning Methods for Single-Cell Multi-Omics Spatiotemporal Data

Postdoct Position in the Development of Machine Learning Methods for Single-Cell Multi-Omics Spatiotemporal Data

Institut Pasteur

Paris, France

Single-cell high-throughput sequencing technologies generate unprecedented volumes of molecular data at cellular resolution, opening new avenues for the application of machine learning to fundamental biological problems. The postdoc to be recruited to join the Machine Learning for Integrative genomics team at the Institut Pasteur as part of the ERC Starting Grant MULTIview-CELL, will be working on the development of a virtual tissue model bridging cell-cell communication and gene-gene interactions by exploiting spatial transcriptomics data and network-theoretic approaches.

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: 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.