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diagnostic tools to support the managements of patients with stroke. This is a unique opportunity to work in a multi-disciplinary, collaborative environment at the interface of AI, machine learning
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. This data will help shape paediatric-specific T2T endpoints and facilitate the development of personalized treatment strategies. You will apply advanced statistical and machine learning methods, and your
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and esteemed industrial and governmental partners. Your expertise in machine learning, statistical analysis and programming will drive impactful research aimed at solving real-world challenges. Why join
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An opportunity to lead work on materials design and discovery for an exceptional researcher with experience in developing and applying Artificial Intelligence and Machine Learning tools to materials
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analysis methods; risk prediction models/ machine learning/ causal inference methods/ signal and data processing and optimisation. You will be enthusiastic and committed to working in a field of health
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both machine learning and symbolic AI. The discovery of new solid electrolytes is a core project target. You will have a PhD in Chemistry, Physics or Materials Science. The post is available from 1 May
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¿. This team is developing a new approach to materials design and discovery that combines experiment with computation, exploiting both machine learning and symbolic AI. Experimental materials synthesis is the
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the development of machine learning and AI software solutions for the prediction of atrial fibrillation in patients after stroke. There will opportunities for involvement in other health data research projects
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structure prediction and machine learning) and is funded through the Leverhulme Research Centre for Functional Materials Design. For example, we work closely with experts in computer science, and the
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. biodegradability) of materials developed by researchers at University of Oxford. We seek a Postdoctoral Researcher to lead the development of automated (machine-learning assisted) methods for reaction monitoring and