Organisation/Company: CNRS
Department: Institut Montpelliérain Alexander Grothendieck
Research Field: Mathematics History » History of science
Researcher Profile: First Stage Researcher (R1)
Country: France
Application Deadline: 30 Nov 2024 - 00:00 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 35
Offer Starting Date: 2 Dec 2024
Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure? No
Offer Description
Identification of the clonal composition of a tumor from panel-sequencing data.
The postdoc is part of the ANR Identhic project, focused on reconstructing the evolutionary history of tumors from partial sequencing data. The candidate will interact with the 4 main members of the project as well as with masters and PhD students working on complementary aspects of the project.
The candidate will be responsible for:
Familiarization with data, data curation, annotation of patient samples.
Development of deconvolution methods to infer clonal composition from full sequencing data.
Benchmarking of the method.
Extension of methods to partial sequencing using biology-informed priors, development of imputation methods in this context.
Implementation of the methods in efficient codes.
Presentation and discussion of the results with members of the consortium.
Manuscript drafting for dissemination of the results in the community.
Participation in workshops and conferences.
The recruited researcher will carry out research tasks in IMAG, within the EPS team under the supervision of Alice Cleynen and Sophie Lèbre. Located on the Campus Triolet of the University of Montpellier, IMAG comprises about 170 members and is structured into 4 research teams: Analysis, Numerical Analysis, and Scientific Computing (ACSIOM), Didactics and Epistemology of Mathematics (DEMA), Probability and Statistics (EPS), and Geometry, Topology, and Algebra (GTA).
Minimum Requirements:
PhD in biostatistics or bioinformatics.
Taste for programming, particularly fluent knowledge of R or Python.
Prior usage of at least one type of sequencing data.
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