163 avenue de Luminy
13288 Marseille 09
Recently, the advancements in single-cell measurement enabled the description of immune cells in unprecedented details. The multi-parameter cytometry currently allows for measuring over 20 markers with polychromatic or spectral cytometry and over 40 markers with mass cytometry per cell. Yet another recent technology, the single-cell RNA sequencing, enables to analyse the whole transcriptome on the cellular level which translates into tens of thousands parameters per cell.
These information rich datasets present both new challenges and opportunities as they allow to find answers to subtle questions about cells’ functions, origin, trajectory of development, characteristics of disease-associated cells, etc. This is however impossible without concurrent advances in computational methods.
The candidate will work on development and implementations of analytical tools for high-dimensional (varying from a dozen to tens of thousands dimensions) data analysis in close cooperation with biologists and data scientists. Specifically the work will focus on the study of low-dimensional combinatorial manifolds (points, graphs and combinatorial surfaces) arising from high-dimensional measurements (point-clouds) of human and murine immune cells. The candidate will work with concepts from various fields of computational science including (and not limited to) cluster analysis, high-dimensional linear algebra, computational topology and deep-learning. The candidate’s duties will include (depending on candidate’s theoretical background and professional interests) algorithmization/coding, literature research, method development and the interpretation of biological datasets.
Solid coding skills (including working knowledge of lower level languages such as C/C++) and solid background in linear algebra. Basic notions of abstract algebra and algebraic topology would be beneficial.
Workstation, access to two linux servers (development and production)
 Hwang, B., Lee, J. H., & Bang, D. (2018). Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental and Molecular Medicine, 50(8).
 Arvaniti, E., & Claassen, M. (2017). Sensitive detection of rare disease-Associated cell subsets via representation learning. Nature Communications, 8(2015), 1–10.
 Edelsbrunner, Herbert and Harer, John, 2010. Computational Topology – an Introduction