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) Machine Learning and Combinatorial Optimization (subject to personal qualification employees are remunerated according to salary group E 13 TV-L) starting at the earliest possible date. The positions are
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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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, and environmental impact Development of dynamic process models for electrified methanol processes under fluctuating power supply from renewable sources Simulation and optimization of the operating
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, manufacturing and optimizing gene vectors Characterization of vector transduction in vitro and in vivo Radiolabeling of gene vectors for PET/CT imaging Animal studies in collaboration with other researchers and
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, optimization and modelling tools; Computational Fluid Dynamics (CFD)), Product and Processes Engineering (Space Engineering), Condensed Matter Physics (Fluid mechanics and dynamics) and Applied Physics
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interest in agentic AI, online learning and optimization, and applications in economics. The full-time positions (100%) are initially offered for two years, with the possibility of extension, depending
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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
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: Algebraic geometry and number theory Area 3: Stochastics and mathematical finance Area 4: Discrete mathematics and optimization Area 5: Discrete geometry Area 6: Numerical mathematics Area 7: Applied analysis
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changes), Computer Science and Informatics (Numerical Analysis; simulation, optimization and modelling tools; Computational Fluid Dynamics (CFD)), Product and Processes Engineering (Space Engineering
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finalization of the foundations of dual-tracer imaging using a GATE-based Monte Carlo simulation Implementation of the developed algorithms within our modular, C++-based and cluster optimized PET image