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quantum attacks Verifiable and privacy-preserving machine learning (e.g. proving model integrity or fairness without revealing training data) Scalable ZK-proof constructions for federated and
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and artificial intelligence/machine learning may be applied are encouraged to apply. If you are interested in research in brain-machine interfaces, and are unsure about whether you have the right
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extent of disruption required to stop unsafe behaviour, and the reliability of proposed protective measures. The emphasis can be adapted to the student's background, including machine learning
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, and selection indicators. Alongside expert-set priors, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way
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/machine learning with traditional approaches to developing and optimising semiconductor materials. You will be trained in one of the world's leading centres of excellence conducting research at the