Researcher in biostatistics/bioinformatics (postdoc)

 CDD · Postdoc  · 24 mois    Bac+8 / Doctorat, Grandes Écoles   TIMC (CNRS) · Grenoble (France)  2663.79 and 3953.59 € per month regarding experience

 Date de prise de poste : 1 février 2022

Mots-Clés

tumor heterogeneity, omic data, biological network, deconvolution

Description

High throughput multi-omic cancer studies have described the inter-tumor heterogeneity and led to well defined molecular classifications. Nevertheless, these classifications only reflect the most abundant tumor subtype in the examined sample, thus neglecting intra-tumor heterogeneity, a major source of therapeutic resistance. As advanced microdissection techniques to isolate a cell population of interest from heterogeneous clinical tissue are not feasible in daily practice, bioinformatic tools to estimate intra-tumor heterogeneity are therefore urgently needed.
Our aim is to develop integrative models of tumor heterogeneity and to infer biological behavior and associated clinical indicators using new computational methods based on Artificial Intelligence. We propose to take up this challenge by developing original methods applied to the study of heterogeneous tumor samples using benchmark datasets.
From the methodological point of view, this work will provide a significant contribution to signal processing of biological data including the integration of noisy multi-omic data and the investigation of spatial heterogeneity. From the clinical point of view, we will provide robust heterogeneity quantifiers that will open up original perspectives for the adaptation of patient monitoring, in particular concerning the diagnosis and treatment of pancreatic cancer.

The successful candidate is expected to develop an approach inspired by machine learning methods to address the problem of subtype classification accounting for intra-tumor heterogeneity. He/She will assess the impact of multi-omic data integration and feature selection in tumor heterogeneity quantification. He/She will develop methods that rely on (i) bioinformatic analysis and integration of transcriptomes, methylomes and chromatine organisation data coming from original tumor surgical samples and (ii) multimodal network analysis. Finally, He/She will apply the developed algorithms to large tumor cohorts and decipher resistance to standard chemotherapies.

- PhD degree in bioinformatics/biostatistics (experience in -omic data integration and/or network analysis would be considered as an advantage)
- Good communication skills that allow productive interactions with an interdisciplinary team (including computer scientists, biologists and cancer pathologists)
- Programming skills (R, Python, bash), prior experience with relevant analytical software and related packages, knowledge of biostatistics would be appreciated
- Experience in artificial intelligence and machine learning would be appreciated
- Ability to communicate in both spoken and written English
- Autonomous and rigorous with a critical mind, ability to handle and analyze large and various data sets with biological and clinical information
- Prior experience with cancer is not mandatory

The CNRS stands for National Center for Scientific Research, it is a French public organization for scientific research. With more than 1,100 research laboratories spread across the country, it is considered one of the largest research organizations in the world. The candidate will be hosted in the MAGe (Methods and Algothims for Genomics) team in the TIMC laboratory in Grenoble, a multidisciplinary lab that gathers scientists and clinicians towards the use of quantitative science for understanding normal and pathological processes in biology and healthcare. MAGe team is composed of senior researchers, postdocs and students with diverse expertise in bioinformatics, genomics, and biostatistics.
The project will be supervised by Magali Richard, a CNRS researcher. The project involved close collaborations with Yuna Blum (a CNRS bioinformatics researcher at the Rennes University) and Jerome Cros (a cancer pathologist at the hospital Beaujon, Paris).
The postoctoral position is funded by the ITMO cancer (aviesan), which promotes applications of data science and artificial intelligence in several scientific domains. In particular, it seeks to leverage Interdisciplinary approaches in oncogenic processes and therapeutic perspectives and to foster contributions of mathematics and informatics to oncology.




Candidature

Procédure : Candidates must imperatively apply here: https://bit.ly/3D01Xmp More info on our group here: https://magrichard.github.io/

Date limite : 17 décembre 2021

Contacts

Magali Richard

 maNOSPAMgali.richard@univ-grenoble-alpes.fr

 https://bit.ly/3D01Xmp

Offre publiée le 29 novembre 2021, affichage jusqu'au 1 février 2022