Postdoc in Computational Systems Biology and Metabolic Modeling

Descrizione del posto

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 Profil 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 Benefits 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