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knowledge in discrete mathematics and in one area of mathematical optimization (e.g., discrete optimization, convex optimization) strong interest in basic research using rigorous mathematical methods very
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beamlines Method Development: develop and optimize methodologies to study nanoparticles encapsulated in protein cages implement improvements to existing protocols and instrumentation to enhance research
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: Optimization of Innovative Air Mobility Networks Operating Supervisor: Prof. Dr.-Ing Hartmut Fricke, Chair of Air Transport Technology and Logistics and co-supervised by at least one additional
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in energy system optimization models including, e.g., reservoir productivity predictions, novel surface processes for CRM extraction, CO₂ reinjection, and reconversion of decommissioned oil wells
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simulations for plume-in-grid models Requirements: university degree (MSc or equivalent) in the field of aerospace engineering, physics or similar solid knowledge of Physical and Analytical Chemistry (phase
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used