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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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. 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
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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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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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up researcher time. You will have a key role in developing the machine-learning methods and will closely collaborate with chemists in the Cooper group and other collaborators. You should have a PhD in
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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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¿. 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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machine learning approaches. You will work alongside project investigators and all partners to maximise the potential from this unique data set to answer key questions on Arctic ecosystem function. You