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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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help develop and characterise advanced patient-derived tumour models and use them to test promising therapeutic targets that exploit vulnerabilities caused by loss of the SMARCB1 gene. This role offers
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of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, general pre-trained transformers, prompt engineering, knowledge graphs, knowledge
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. 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, provided at the bottom of the next page after
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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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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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within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical questions and is aimed at novelty, understanding of physiology and
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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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will increase to Grade 6. Desirable criteria Experience of working with clinical trial samples under GCLP guidelines Experience of analysing complex single-cell data sets Downloading a copy of our Job
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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