Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Laboratoire d’Informatique et Systèmes
Durée : 2
Contact : thierry.artieres@lis-lab.fr
Date limite de publication : 2021-01-07

Contexte :
A postdoc position is available on : “Dynamic multi-domain translation for high-throughput sequencing data”

Lead supervisor: Paul Villoutreix (CENTURI, LIS)
Co-supervisor: Thierry Artières (ECOLE CENTRALE MARSEILLE)

The postdoc will take place in Paul Villoutreix’s
interdisciplinary team (Learning meaningful representation of life http://bioml.lis-lab.fr/) and the Machine
Learning team of the Computer Science lab in Marseille (https://qarma.lis-lab.fr)

Sujet :

Project description:

Multi-domain translation is a problem of major interest in biology. When studying a biological system such as
a developing embryo, many acquisition techniques are available. Each of them brings out unique features of
the system, however, they are often incompatible and cannot be performed at the same time. To overcome
this challenge we need to develop multi-domain integration techniques. Current approaches rely either on
the tools of optimal transport, or multiple autoencoders, however, they are not designed to address temporal
data. With this project, we propose to take advantage of multi-domain dynamical data in high-dimensional
spaces to infer a dynamical coupling between sequencing data acquisition techniques (such as sc-RNASeq)
and microscopy data. This will include theoretical work and computational experiments on artificial and real
data. The results of the project are expected to have large impact in the machine learning community and be
of wide applicability in real world biological problems. The scientific environment for this project is ideal as it
combines expertise in interdisciplinary approaches of machine learning applied to biological data, and
expertise in theoretical machine learning.

References
Towards a general framework for spatio-temporal transcriptomics
Julie Pinol, Thierry Artières, Paul Villoutreix, NeurIPS, LMRL workshop, 2020
Gene expression cartography
Nitzan, Mor, et al., Nature, 2019
Multi-domain translation by learning uncoupled autoencoders
Karren D Yang, Caroline Uhler, Arxiv, 2019

Profil du candidat :
Expected profile

We are looking for a PhD in machine learning, computer science, applied mathematics with strong interest in
machine learning and its applications to biology.

Formation et compétences requises :
We are looking for a PhD in machine learning, computer science, applied mathematics with strong interest in
machine learning and its applications to biology.

Adresse d’emploi :
LIS Lab, Marseille