The CRIStAL Lab - Lille (www.cristal.univ-lille.fr) has an immediate opening for a postdoctoral position in the field of machine learning for cancer systems biology.
The successful candidate will be offered an initial contract for 24 months.
We invite applications from researchers who hold a PhD in machine learning, biostatistics, computational system biology or a related field.
About the project
The successful candidate will join an interdisciplinary team of computational and molecular biologists, working on a cancer system biology project (involving Radvanyi lab - institut Curie and Southgate lab - York University) : LIONs (Large-scale Integrative approach to unravel the relationships between differentiatiON and tumorigenesiS ) funded by ITMO cancer/ INSERM (lions.issb.genopole.fr).
She/He will be responsible for developing machine-learning algorithms for regulatory network inference and interrogation. We will use high-throughput heterogeneous data (miRNA and mRNA isoform expression) and protein data of bladder tumors and normal urothelial cells at different stages of proliferation/differentiation to infer, analyze and compare the regulatory networks — transcription factors, miRNA and target genes — found in the normal and pathological states.
This work will build on the previous studies carried out by our team (i3-bionet.issb.genopole.fr) to model gene regulation in both discrete and a continuous frameworks. It will also involve extensive ci-regulatory element analysis and tools for mathematical studies of a perturbation model combining inferred normal and tumoral regulatory networks with heterogeneous tumor data (DNA methylation, mutations and genomic alterations). Key candidate genes from deregulated pathways encoding regulators or therapeutic targets will be validated functionally.
We invite applications from researchers who hold a PhD in machine learning, biostatistics, computational system biology or a related field.
Applicants should send a full CV, the contact details of three referees, and a cover letter to:
Dr. Mohamed Elati (www.issb.genopole.fr/~elati/)
Email: mohamed.elati@univ-lille1.fr