Post-Doctoral Researcher – Computational workflows for metabolic flux analysis by stable isotopes in Zürich

The Institute of Molecular Systems Biology at ETH Zurich is inviting applications for a Postdoctoral position in the group of .

The group of Prof. Zamboni has a long history in the development and use of tracing experiments for the analysis of cellular metabolism. The approach relies on the supplementation of isotopically labelled metabolites. These include stable isotopes (e.g. 13C, 2H), whose structure gets scrambled under the action of metabolism. If appropriately designed, the resulting labelling patterns indicate the activity of intracellular metabolic pathways and reactions. Quantitative information about fluxes is obtained by measuring the labelling patterns by mass spectrometry and by mathematical inference with a model that accounts for all possible atomic transitions. However, to date, the adoption of tracer studies by non-expert groups is hindered by the often prohibitive need for expertise and technical means.

The Zamboni lab is aiming at simplifying tracer studies for clinical applications. While most elements of the workflow have been established over the past decades, the challenge is to assemble all pieces in an integrated and automatised workflow to support routine analyses. The project demands the integration of experimental and computational developments. This position covers the computational and IT work of the project. Beyond mere flux estimation, the tasks include all the elements essential to automatise the whole workflow, from raw mass spectrometry data to reporting of results. Developments will be defined and verified in regular meetings with stakeholders from clinical and research labs. The initial appointment is for two years.

You will lead all computational activities necessary for estimating fluxes from MS data, including the optimal design of tracers and LC-MS assay. Furthermore, you will integrate means for quality control and quality assurance of input data, as well as build user-friendly interfaces for data submission and reporting. Implementing computational modules systems in containers for automated deployment on local or cloud infrastructure will also be a duty of your job. 
You will cooperate and communicate tightly with colleagues and clinicians performing the experiments to identify and remove bottlenecks and bugs. Foster open communication with team members, stakeholders, and potential users. You always aim for the highest quality of service for end users and you will roll out software releases regularly for testing.

• Doctoral degree in computer science, bioinformatics, applied mathematics or equivalent.
• Ample experience in machine learning, Bayesian statistics, and possibly simulation-based inference.
• Deep understanding of the mathematical paradigms used to calculate fluxes from mass spectrometry data (e.g. refs , , , ). Practical experience on this topic is considered a differentiating asset.
• Experience with building and deploying containers, workflow managers (e.g. snakemake and nextflow), application servers, and data visualisation is advantageous.
• Passion for metabolism, ambition to impact clinical practice, and entrepreneurial spirit.
• Proactive, solution-oriented attitude and excellent communication skills.

ETH Zurich is a family-friendly employer with excellent working conditions. You can look forward to an exciting working environment, cultural diversity and attractive offers and benefits.