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of high-current power converters. Designing circuits, prototyping and testing in the lab. Any candidate who: has a good grasp of various power circuit topologies for DC-DC converters; is familiar with power
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predictive maintenance models combining physical and ML approaches. Test, validate, and integrate developed solutions in real industrial environments. You must have a two-year master's degree (120 ECTS points
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to the development of machine learning frameworks You are eager to elaborate on the newest research results, improve ideas, and test/validate by engagement with national and international collaborators You will have
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of solvers for stochastic optimization problems, and test the methods on real-life data. As part of the PhD you will be following advanced courses to extend your skills, implement and test algorithms, and
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, synthesis, characterization and test for sustainable energy solutions We also have research activities in our three interdisciplinary centers relating to nuclear energy, catalysis, and visualization
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, laboratory testing and data augmentation. Your main goal will be to develop predictive models for assessing the degradability of bioplastics, facilitating a transition to more sustainable materials. More
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achieve a substantial 30-50% reduction in associated energy consumption. The new system developed in this project will be tested in different climates across Europe. You will have the opportunity to join
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aggregation starts and propagates via phase separated condensates — by developing dynamic, life-like experimental frameworks in test-tubes. The research also aims to challenge existing decades-old kinetic and
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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to: Design and conduct research projects in collaboration with the supervisory team Examine and analyse data on startups supported by BII Collect and analyse additional quantitative and qualitative data from