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better computer simulations of the brain. The lab will leverage these advancements to disentangle and model behaviourally-relevant visual and semantic dimensions of visual cognition in the human brain
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programme aims to advance fundamental understanding of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key
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from open water and polynyas drive snowfall and melt events; and running regional climate model simulations to project future Greenland surface mass balance under different sea ice scenarios. You will
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technologies, drug discovery and advanced therapeutics. We have excellent facilities to support our parallel use of pre-clinical cell and in vivo models, alongside human cellular models, which provide
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technologies, drug discovery and advanced therapeutics. We have excellent facilities to support our parallel use of pre-clinical cell and in vivo models, alongside human cellular models, which provide
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. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process & microstructure with
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Simone Ruggeri. You will also collaborate with a PhD student working at TU Delft, who will use molecular modelling to simulate the milk fat globules and plant lipid droplets and guide the experimental work
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use molecular modelling to simulate the milk fat globules and plant lipid droplets and guide the experimental work. Your qualities We are looking for a highly motivated candidate with a background in
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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of the genotypes. The PhD student will work together with a postdoc on the project. Description of the scientific environment The PhD student will be hosted in the Crop Physiology and Production Systems research