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Field
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behaviour of so-called yield stress fluids, which keep their shape like solids at low loads, yet flow like a liquid at larger loads. One possible focus could be on the dynamical process whereby a material in
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project is to develop a series of surrogate models focusing notably on Physics-Informed Neural Networks to emulate the process of sediment deposition, diagenesis, and potentially fracturing, working closely
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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the successful software package VASP (Vienna ab-initio Simulation Package). Currently, the group consists of two full professors, one associate professor, several postdocs and about 10 PhD and master students
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programming skills, in depth knowledge of MR theory, and experience with the acquisition and analysis of (preclinical) MRI data, as well as with mathematical modelling and image processing methods. Experience
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vivo, in vitro and analytical methods. Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found in the Job Description document
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density functional theory 3. Experience in cyclic voltammetry experiments 4. Characterisation of paramagnetic compounds 5. Evidence of independent funding Downloading a copy of our Job
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the United Kingdom and abroad to conduct research and attend conferences 3. Experience in giving talks at internationally recognised conferences and workshops 4. Some teaching experience Downloading a copy
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Postdoctoral researcher in the field of the Life Cycle Assessment (LCA) of bio-based processes and products The postdoctoral researcher will work as expert in LCA for bio-based processes and products from wood
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Institute, a pioneering biomedical research institute in London where our wet lab is located. We are also part of Cancer Research UK City of London Centre, which sponsor our high-performance computer cluster