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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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is expected between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects
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based within the School's research environment, working with an academic project team on robotics, machine learning and cyber-security. The project is supported by the University's research and
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. Experience in machine learning and AI, including experience in machine learning software development. E3. Experience in training deep learning models relevant in research projects. E4. Experience of applying
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research skillset in novel machine learning approaches, we are recruiting a team of outstanding researchers in cloud compute architecture design and event-triggered computing to work with us on
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identification of safety liabilities. You will harness modern machine learning techniques to extract data from a range of relevant resources, such as clinical trials, publications and drug labels
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identification of safety liabilities. You will harness modern machine learning techniques to extract data from a range of relevant resources, such as clinical trials, publications and drug labels
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craniofacial identification research and machine learning. You will require a computer science background. You will be applying AI and/or machine learning to Face Lab processes in relation to craniofacial
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, Artificial Intelligence, Data Science, Electronic Engineering or a closely related discipline. You will have practical experience of Python for machine learning or data analysis, including experience of
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and adaptive cybersecurity solutions for shared social and economic systems. The successful researcher will apply expertise in programmable networking, network traffic analysis, and machine-learning