Your position
The candidate will have the opportunity to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical analysis of large datasets, high-throughput metabolomics, time-lapse microscopy, to investigate how to interfere with fundamental mechanisms in the regulation of cancer metabolism. Available resources at the department include a mouse facility, high-end FACS, IT and microscopy facilities, the Life Sciences Training Facility (for gene expression and proteome profiling) and much more.
The candidate is expected to have a strong background and interest in quantitative disciplines, excellent teamwork and communication skills in English. The candidate will have the opportunity to develop the following project with a lot of academic freedom and strong support from senior members in the lab, and at the same time to play an active role in shaping and creating an inspiring research and working environment.
In line with our and Uni Basel values (
https://www.unibas.ch/en/Research/Values-Ethics), we are committed to sustain and promote an inclusive culture, ensure equal opportunities and value diversity and respect in our working and learning environment.
The key aim of this project is to systematically map the metabolic profile of a large biobank of lung tumor specimens and link specific metabolic signatures to tumor characteristics, prognosis, and treatment response using high throughput metabolomics integrated with clinical data. The project seeks to identify drugs that can target and potentially reverse disease-associated metabolic alterations. We are looking for a highly motivated Post-Doc researcher to study lung tumor metabolism (60% wet lab and 40% computational).
Your main tasks will be:
- Adapting existing and developing improved experimental protocols for the high-throughput metabolomics analysis of complex clinical samples.
- Coordinate and perform large-scale metabolomics profiling in lung tumor specimens.
- Apply and develop model-based and statistical approaches to analyze and interpret large compendia of metabolome profiling.
- Integrate diverse large-scale omics profiles to derive experimentally testable hypotheses on patient-specific metabolic vulnerabilities and drug sensitivity.
- Design and implement experimental validation of metabolome-based predictions.
- Support and preparation of scientific reports and journal articles.