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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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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
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mobility of migrants shape local populations? Developing a spatial microsimulation model of population dynamics with application in infectious disease modelling (DynaMIGs)”. DynaMIGs is a four-year
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operational employment. This doctoral research will thus leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modelling systems as graphs
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Genetics, Reproduction and Development (GRAD) Large Research Group to: Use single-cell omics techniques to analyze human pluripotent stem cells, human embryos, and stem cell-derived embryo models. Study how
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for anomaly Detection and diagnostics: Leveraging state-of-the-art machine learning and deep learning models for automated fault detection, classification, and time-till-failure prediction. This will involve
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in AI: Generative Diffusion & 3D/4D Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Semantic-Aware Compression & Network Information Theory
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models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
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, you will leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modeling systems as graphs and encoding nonlinearities in these. As