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-enhanced exact methods, particularly focusing on Column Generation (and Branch-and-Price), to improve scalability and convergence in solving complex optimization problems. In collaboration with your
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shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
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FLOW research group is a young, dynamic group working in the fields of thermodynamics, fluid mechanics, and data-driven modelling. At the Department of engineering Technology (INDI) — Thermo and Fluid
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can be varied. Crucially, the models we derive will be validated by real-world measurements to ensure our simulation environments are realistic and scalable to more complex radar networks. This will
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to ensure AI models deliver reliable, transparent, and auditable decisions in complex industrial contexts. This project offers an exciting opportunity for you to shape the next generation of industrial AI
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Chaperones are protein complexes that assist other proteins in their folding process. Well-known examples are the bacterial GroEL/ES and the eukaryotic TRiC. Despite extensive research, there are still
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such as transportation, defense, mining, and urban development, yet their design is often hindered by complex geological conditions and evolving functional requirements. This project aims to create safer
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. We are seeking a Research Scientist specializing in plasma physics to join our team. In this role, you will: Develop and refine models to understand and predict the behavior of plasmas near the wall
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nurseries, understanding how species are affected by different forest management practices, and modelling how conservation and timber production can be combined over time. The PhD students will be employed by
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will transition in a second phase to white box approaches that result in interpretable models. For ground truth data, μCT data will be used. A similar approach will be applied using surface roughness