The institute of Systems and Synthetic Biology (Paris region) has an opening for a postdoctoral position in the field of machine learning for systems biology.
The successful candidate will be offered an initial contract for 24 months with possible extension for an additional 12 months depending on performance.
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 : LIONs (Large-scale Integrative approach to unravel the relationships between differentiatiON and tumorigenesiS: http://lions.issb.genopole.fr ) funded by ITMO cancer/ INSERM.
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 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.
About iSSB
The iSSB is part of Genopole, the premier French BioPark of leading research academic and private laboratories in biotechnology, genomics, post-genomics and related sciences. It is located in Evry (Paris region).
How to apply?
Applicants should send a full CV to:
Dr. Mohamed Elati (http://www.issb.genopole.fr/~elati/)
Email: [email protected]
The successful candidate will be offered an initial contract for 24 months with possible extension for an additional 12 months depending on performance.
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 : LIONs (Large-scale Integrative approach to unravel the relationships between differentiatiON and tumorigenesiS: http://lions.issb.genopole.fr ) funded by ITMO cancer/ INSERM.
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 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.
About iSSB
The iSSB is part of Genopole, the premier French BioPark of leading research academic and private laboratories in biotechnology, genomics, post-genomics and related sciences. It is located in Evry (Paris region).
How to apply?
Applicants should send a full CV to:
Dr. Mohamed Elati (http://www.issb.genopole.fr/~elati/)
Email: [email protected]