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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are looking for a PhD student who would like to optimize the use of real-world data (RWD
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CHP units, the wider energy system risks losing a crucial flexibility resource. Through strategic integration hybrid energy storage systems with greenhouse operations via optimization methods, control
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specialists and set clear priorities to realize projects effectively and on time, building infrastructure that makes complex analyses faster and more efficient. Your team optimizes virtual computing power (GPUs
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are focused on areas with high noise exposure: areas near the runway or final approach or early departure routes. Current noise models only consider a free propagation path from the sound source towards
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, patient motion, and more. Today, these parameters are either manually configured, heuristically optimized, or compensated post hoc using multi-level calibration scans or corrections, which introduces
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PhD position ‘Courage to Correct: Balancing Error Prevention and Learning in Strategic Crisis Teams’
demonstrate that treating mistakes as learning opportunities enhances performance (Horvath et al., 2023). However, crisis contexts differ: what counts as a ‘mistake’ is often ambiguous in the moment. Rather
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staff position within a Research Infrastructure? No Offer Description Do you want to contribute to finding practical solutions for optimized nutrient adequacy, when shifting towards more plant-based diets
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capabilities, existing technology can only handle relatively small-scale problems. Information In the SymBi project (Exploiting Symmetries for Faster Bilevel Optimization Algorithms), we address this limitation
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of Cologne (6 months), and at the optimization software company MOSEK (1 month) in Copenhagen. At the University of Cologne, the secondment will be supervised by Professor Frank Vallentin. The CentER graduate
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approaches treat NP design as static property prediction. This project takes a fundamentally different approach: using generative models to propose novel NP formulations and coupling them with explainability