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Post-doctoral Fellow in Computational Biology, Luxembourg Institute of Health


Date & time Jan 22
Rue de l'Institut Molitor, Attert
Creator thepostdoctoral
Category deadline


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Post-doctoral Fellow in Computational Biology, Luxembourg Institute of Health

In this interdisciplinary project, the lab aims to investigate treatment resistance mechanisms in Glioblastoma (GBM), the most aggressive and incurable brain tumour. GBM cells display very strong intrinsic plasticity and adapt reversibly to dynamic microenvironmental conditions, forming a very dynamic ecosystem. The lab will investigate how this high plasticity allows GBM cells to adapt towards drug-resistant states upon treatment. They will interrogate intra-tumoral heterogeneity at the single-cell level, taking into account tumour cells and supporting tumour microenvironment.


LIH are looking for a computational scientist with strong interest in cancer biology to conduct advanced transcriptomic data analyses (bulk and single cell RNA-seq), with the focus on deconvolution methods. The objective is to identify signatures of treatment tolerant and resistant states.

Training and research environment

NORLUX lab is a multinational and interdisciplinary team focusing on the biology of malignant brain tumours. MODAS brings expertise in computational biology. Both teams are a part of the Department of Cancer Research and use state-of-the-art preclinical models combined with cutting-edge molecular technologies and machine learning tools. The post-doctoral fellow will be supported through an FNR CORE funded project and will join an interdisciplinary project team, including biologists, animal experts, bioinformaticians and statisticians. Recent related references: https://pubmed.ncbi.nlm.nih.gov/33009951/https://pubmed.ncbi.nlm.nih.gov/31533822/

Key skills, experience and qualifications
  • PhD in computational biology, bioinformatics, genomics, systems biology, molecular biology or a related field.
  • Prior experience in cancer research is an asset.
  • Expertise in computational and statistical analysis of transcriptomic data (RNA-Seq, scRNA-seq) is required.
  • Experience in deconvolution methods and/or machine learning is an asset.
  • Understanding of common bioinformatics approaches and extensive experience with one of the main programming languages for data analysis (R, Python). Knowledge of other programming languages and data analysis tools is an asset.
  • Independent and self-motivated person, scientific creativity and originality, strong team spirit and collaborative capacity, excellent time management, rigour, perseverance, strong writing skills.
  • Fluency in English is mandatory.

Applications including a cover letter and a curriculum vitae should be sent before 15 January 2022 through the LIH website via the apply button at the bttom of this page. Please apply ONLINE formally through the linked web page. Applications by email will not be considered.


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