Postdoc in Computational Systems Biology and Metabolic Modeling

The position is a postdoctoral fellowship focused on computational systems biology and metabolic modeling within the Insel Gruppe, Switzerland’s largest integrated medical care network that includes the Inselspital, Bern University Hospital and several regional hospitals. In this role you will design and implement mathematical, statistical and machine‑learning frameworks to interrogate complex, high‑dimensional and multimodal biological and clinical data, including spatial and imaging information. You will construct and simulate models of lipid and nutrient metabolism using compartmental, kinetic, mechanistic, network‑based or physiologically based strategies, rebuild tissue‑specific metabolic maps and test model outputs against experimental results. Daily work also involves contributing to interdisciplinary research teams, preparing and delivering presentations at internal meetings and international conferences, and publishing findings in peer‑reviewed journals. Candidates must hold a PhD (or equivalent) in computational biology, systems biology, applied mathematics, biochemical engineering or a closely related discipline. Required expertise includes advanced mathematical and statistical modeling, machine‑learning applications, and systems‑biology approaches applied to high‑dimensional or multi‑omics datasets. Proven experience with in‑silico metabolic modeling—such as constraint‑based, genome‑scale, network reconstruction, kinetic or flux simulations—is essential, as is proficiency in R, Python or MATLAB and the ability to merge experimental data with computational analyses. Strong analytical abilities, independence, teamwork, and excellent written and spoken English are also mandatory. Experience with mass‑spectrometry, spatial omics, molecular or tissue imaging is considered a valuable addition. The appointment offers a collaborative, cutting‑edge research setting, access to large pre‑clinical and clinical datasets, and close interaction with experts across scientific and clinical fields, supporting both scientific growth and professional development.