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Computational Fluid Dynamics (CFD), fluid mechanics, and Artificial Intelligence (AI), with a particular focus on developing deep reinforcement learning methods for active flow control of hydraulic
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. This limits our ability to predictably control and exploit the drop for useful tasks. Aims: 1. Develop computational models to quantitatively predict the response of chemically active drops to the
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. The research work will have a cross-disciplinary nature and will involve a range of techniques including high-throughput testing on microbial cultures using automation, operation of bioreactors, flow
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arise due to local internal energy injection. Likeconventional liquids, the macro-scale confinement, or boundaries, of thfluid can control their flow patterns, and the micro-scale behaviour of the
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communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results into applications in close collaboration
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homeostasis in immune cells, as well as immune control of systemic lipid metabolism. Our group utilizes cross-disciplinary approaches, including fundamental biochemistry, cell biology, immunobiology