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Description The field of combinatorial optimization is concerned with developing generic tools that take a declarative problem description andautomatically compute an optimal solution to it. Often, users
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renewable energy generation.KU Leuven leads Modelling and Optimization, which focuses on: Developing hybrid models combining first-principle and machine learning approaches. Creating predictive frameworks
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optimized designs in actual resonant power converters using WBG semiconductors. The expected outcomes include a robust 3-D modelling framework, novel low-leakage magnetic layouts, and an advanced design
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their power consumption, select the most appropriate options and dimension them optimally. This also supports the development of run-time control mechanisms maximizing the transceiver energy efficiency in
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, Research Group Musculoskeletal Rehabilitation, we are looking for a part-time doctoral assistant, who will collaborate on a project concerning optimization of the guidance of young (elite) athletes
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to support this in the most efficient way. Ultimately, deploying and optimizing different base stations types on different sites may require AI solutions to master the multi-dimensionality of related scenarios
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based on extensive and realistic end-to-end simulations, and optimize them for the best performance/complexity trade-offs. By doing so, you will enable hybrid PHY implementations combining traditional and
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must hold a Master degree in Electrical Engineering (or equivalent), have a solid mathematical background (e.g. in control theory and optimization) and have taken specialized courses in at least one of
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advanced causal inference, robust counterfactual simulation, and protocol optimization analytics. Key tasks include developing emulation engines, establishing regulatory-grade validation pipelines, and
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experience in design of (analog/mixed-signal) integrated circuits. hands-on experience with methods of optimization and circuit design and layout. Candidates should be motivated, independent, show critical