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to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information. About
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment
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organisational, communication, and interpersonal skills. You will bring: Strong experience applying AI methods to complex data, ideally healthcare data A record of high-quality publications and presentations
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& robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees
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, as well as for their babies. You will work with large, complex datasets from national surveys and routinely collected health data to support impactful research. You will manage your own research
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dietary exposure risks to consumers. The research will involve method development using LC-MS/MS for toxin detection, sample collection, data analysis, and comparison of results against current safety
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research frontier in computer vision that combines three critical challenges: class imbalance, recognition of rare and unseen species, and dense labelling of high-resolution imagery. The candidate will
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PhD Scholarship aims to evaluate performance of current GRSs and optimise performance in Black men. The aim is to deliver data-driven evidence to guide future policy implementations of GRSs. The project
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Research at the LMB aims to understand the workings of complex systems, such as the immune system and the brain, and use this information to solve key problems in human health and disease
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural