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for structural analysis of metabolites and lipids, creating metabolomics knowledge graphs, and inferring fluxes from MS data. The group collaborates with mass spectrometry vendors and technology partners
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research questions. The ESA group is headed by Prof. Dr. Russell McKenna and generally focuses on the optimization and assessment of complex systems and infrastructures with an emphasis on environmentally
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a grant from the Swiss National Science Foundation entitled Machine Learning for Optimized Ab-initio Quantum Transport Simulations (MALOQ). It officially started on January 1st 2026 and will
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integrating various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large
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, and cell biology. Job description Design, implement, optimize, and maintain automated laboratory workflows for life science applications Provide expert support for laboratory automation platforms (e.g
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computationally and developing scientific software. Experience in Python is highly recommended, additional knowledge of performance-oriented modeling frameworks, either based on Python (e.g., JAX, Pytorch) or other