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(preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models to the clinic The post holder will be based in the Department
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of the microbiome in olfaction using mouse models. The selected applicant will join the vibrant and friendly Tucker lab and work as part of a team interacting with the group of Prof Mike Curtis. The postdoc will
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techniques. Additional expertise in studying autophagy and using preclinical mouse models of cardiovascular disease are highly desirable. This is a full time post (35 hours per week), and you will be offered a
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cardiovascular disease progression and outcomes. The successful candidate will work to identify key clinical and immunological predictors, develop risk models, and help generate new hypotheses to inform future
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Guy’s Campus, Denmark Hill and UCL. About the role: To investigate the cellular mechanisms underlying schizophrenia-related symptoms in animal models (mice), in the context of a collaborative project with
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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
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of Biomedical Engineering and Imaging Sciences is a cutting-edge research and teaching School dedicated to development, translation and clinical application within medical imaging and computational modelling
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Science, Robotics, AI, or a related field 2. Strong background in machine learning and robotics, with specialisation in one or more of the following areas: generative models, reinforcement learning, human
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better position. A model is needed into which to feed critical information and retrieve cause/effect insights on which to base logical decisions. Biological information cuts across diagnostic boundaries
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-vis and EPR spectroscopies, magnetometry, cyclic voltammetry). Some experience in characterising paramagnetic compounds, modelling organometallic mechanisms, as well as basic density functional theory