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and advanced machine learning. The project will integrate measurements from the SWOT satellite mission with Oxford's Global River Topology (GRIT) hydrography to develop verified, uncertainty-aware
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will be responsible for the follow : (full details of duties available from the Job Description) Research Collaboration and engagement You will have completed a PhD in machine learning, computer science
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, relating to 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
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evidence to support the evaluation of AI and machine learning models. This may include investigating data-centric AI strategies, such as data quality assessment, annotation refinement, dataset curation, and
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modelling, machine learning, or microfluidics. They will also have excellent communication, organisational and problem-solving skills, and a strong interest in interdisciplinary quantitative biology
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contribute to the development and validation of computationally efficient motor-twin models for permanent magnet synchronous machines. The work will focus on machine modelling, parameter and state estimation
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property testing. Cryptography, zero-knowledge proofs, and probabilistically checkable proofs. Locally testable and locally decodable codes. Computational learning theory. Structure-versus-randomness and
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the impacts of extreme heat exposure on learning and decision-making, as relevant to mental health. This is a full-time role, based in Central Cambridge. The primary function of this post is to undertake
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data analysis would be highly advantageous but is not essential. We value reliability, care in laboratory practice and willingness to learn as much as formal experience. Excellent communication and
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centres, we provide an unparalleled learning environment for its 24,000 students and 13,000 staff. At Cambridge, our mission is to contribute to society through world-class education, learning, and research