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to teaching (including development and delivery of CPD training), and research (bringing causal inference and machine learning/AI techniques) to mobilise data into action on prevention and early diagnosis
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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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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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¿. 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