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, advanced imaging techniques and numerical modelling. About the role A successful candidate will be working on the EPSRC funded project New perspectives in photocatalysis and near-surface chemistry: catalysis
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) Experience of working with multiple stakeholders in complex systems. Experience in large scale simulations Experience in Bayesian methods Experience using CRAFTY agent based model Full details of the role and
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of background knowledge; implicit knowledge is derived by performing reasoning over event graphs; and the comprehension model is developed with built-in interpretability and robustness against adversarial attacks
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expertise in organoid models of neurodevelopment to join our team and be involved in a MRC funded project aimed at generating region-specific brain organoids and assembloids from multiple gene-edited human
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schizophrenia-related symptoms in animal models (mice), in the context of a collaborative project with clinicians and computational scientists. This project will be supervised by Prof Oscar Marin and Prof Beatriz
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vitro, organoid co-culture models will be developed using primary human epithelial cells. Candidates should have an excellent research track record, be committed to the project and keen to work in a
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collaboration with Prof. Giovanna Tinetti and her team and collaborators at KCL. The main purpose of this role is to develop new and/or to use existing models to simulate the atmospheres of exoplanets and use
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Clear interest in and knowledge of current understanding of vertebrate development Knowledge of use of chick as a model system Knowledge of limb development and evolution Downloading a copy of our Job
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Science, Robotics, AI, or a related field Strong background in machine learning and robotics, with specialisation in one or more of the following areas: generative models, reinforcement learning, human-centred AI
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of liver micrometastases development in cancer, based on a novel MRI approach which combines multi-dimensional diffusion-relaxometry acquisitions, efficient data denoising and biophysical modelling