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develop federated methods which can handle heterogeneous and inconsistent medical data. This involves harmonization of data from different hospitals (without having all data in one place), developing new
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analysis focuses on the development of high-order accurate, provably stable methods that produce reliable approximate solutions to difficult nonlinear problems. These discretisation techniques include, but
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, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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methods that reduce compute, energy usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is