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Post-Doctoral Fellow in brain tumor metabolism: tumour heterogeneity and resistance mechanisms

Luxembourg Institute of Health (LIH)
Luxembourg (LU)
Closing date
11 Aug 2024

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Job Type
Employment - Hours
Full time
Fixed term

Background. In this interdisciplinary project, we aim to investigate treatment resistance mechanisms in Glioblastoma (GBM), the most aggressive and incurable brain tumour. GBM cells display very strong intrinsic plasticity and adapt their energy production to support tumor growth and survive versatile microenvironments. We will investigate how the tumor cells adapt their metabolic demands during tumor growth and upon treatment. We will focus in particular on the function of mitochondria, the organelles supporting energy production in the cells. Metabolic features of glioblastoma will be compared to other brain tumor entities, including brain metastases.


Objectives. We are looking for a molecular biologist with strong expertise in metabolomics and LC-MS data analysis to conduct functional cancer assays and metabolomics profiling.


Training and research environmentNORLUX lab is a multinational and interdisciplinary team focusing on the biology of malignant brain tumours. The team is 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 INTER funded project established as an international collaboration with the Institute of Biotechnology CAS, Vestec, Czech Republic (PIs: Renata Zobalova and Jiri Neuzil). Post-doc will join an interdisciplinary project team, including biologists, animal experts and bioinformaticians. The project will be carried out in collaboration with the LIH’s Metabolomics platform (Head: Nathalie Legrave) and the Cancer Metabolism Group (PI: Johannes Meiser).


Recent related references:;


Key Skills, Experience and Qualifications

  • PhD in molecular biology, metabolomics or a related field.
  • Expertise in computational and statistical analysis of LC-MS data for targeted and untargeted data processing approaches, stable isotope labelling incorporation and metabolic flux analyses is required. Experience in metabolomics data analysis and statistics tools and software such as TraceFinder, MetaboAnalyst, Compound Discoverer and SIMCA is desirable.
  • Prior experience in cancer research, functional assay or drug testing is an asset.
  • Understanding of common bioinformatics approaches and experience with programming languages for data analysis (R, Python) is a plus.
  • 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.


Researchers are supported by easy access to scientific expertise, well-equipped facilities, an active seminar program as well as opportunities for conference attendance and collaborations with other research organisations.




Scientific contact

Dr Anna Golebiewska;


More information about the groups can be found here:


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