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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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an industrial or research setting. Deep expertise in C++ and SystemC, with a proven track record of developing, integrating, and optimizing TLM 2.0 models. Strong background in computer architecture
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communication and organization skills are warranted. Given the size of the project, we also expect close collaboration with the wet lab team to optimize workflows and procedures and to aid in data quality control
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illumination, this project aims to overcome these limitations and enable a superior imaging methodology. The work will focus on enhancing optical components, designing custom spectral filters, and optimizing
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of stochastic processes and/or optimization. Candidates should have experience with programming of scientific software. Excellent proficiency in English is required, as well as good communication skills, both
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processes and/or optimization. Specific experience with sampling methods for Bayesian is highly appreciated. Experience with cardiac electrophysiological modeling is a plus. Candidates should have experience
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of the project is performance modelling and optimization of 6G radio access networks (RANs) under adverse conditions, resulting from natural disaster or deliberate attacks. The specific focus of your work will be
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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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in process design and process optimization, including model development and numerical simulation (Matlab, Aspen etc.) of advanced adsorption processes such as TVSA and ESA (joule heating, inductive