Inselspital · Healthcare
Postdoc in Computational Systems Biology and Metabolic Modeling
- Develop and apply mathematical, statistical, and machine-learning models to complex, high-dimensional, and multimodal biological and clinical datasets, including spatial and imaging data
- Model lipid and nutrient metabolism using compartmental, kinetic, mechanistic, metabolic network, or physiologically based approaches
- Reconstruct and model tissue-specific metabolic networks and validate model predictions using experimental data
- Participate in and contribute to multidisciplinary and collaborative research projects.
- Prepare, present, and communicate scientific findings at internal meetings, international conferences, and through peer-reviewed publications
- PhD or equivalent in computational biology, systems biology, applied mathematics, biochemical engineering, or a related field
- Strong expertise in mathematical and statistical modelling, machine learning, and systems biology, with experience analysing high-dimensional, multimodal, or multi-omics biological or clinical data
- Demonstrated experience in in silico metabolic modelling, e.g. constraint-based or genome-scale metabolic modelling, metabolic network reconstruction, kinetic or metabolic flux modelling, or data-driven simulation
- Proficiency in R, Python, or MATLAB and experience integrating experimental data with computational models;
- Experience in mass spectrometry, spatial omics, molecular imaging, or tissue imaging is a strong asset
- Enthusiastic and collaborative scientist with strong analytical and problem-solving skills, the ability to work independently and in multidisciplinary teams, and excellent written and spoken English
- A supportive, collaborative, and state-of-the-art research environment with opportunities for scientific and professional development
- Participation in multidisciplinary research projects at the interface of computational biology, systems biology, metabolism, multi-omics, molecular imaging, and clinical research
- The opportunity to work with large datasets from pre-clinical and clinical studies
- Close collaboration with experts from different scientific and clinical disciplines