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models optimised with evolutionary algorithms to address combinatorial optimisation in model design and the noisy nature of climate data. The Doctoral Researcher will receive on-the-job training in machine
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. Exposure to neural-symbolic algorithms for transforming intent into conformant security or safety policy and/or enforcing security controls is optional but beneficial. Research will also give the opportunity
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implementing AI algorithms to deliver safer and more efficient care. The student will have access to a unique training programme in AI in healthcare and health data science as well as a wide range opportunities
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qualifications will be considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++) An interest in physiological
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representation. Key aims include improving the generalizability, interpretability, reasoning and causal grounding of these models, developing new optimisation algorithms with biologically meaningful regularisation
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conduct cutting-edge research on topics including, but not limited to: Complexity theory Quantum algorithms and complexity Sublinear algorithms Interactive proofs, PCPs, and zero-knowledge proofs
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, possibly by extending it to the relevant derived categories. The successful applicant will join the very active and supportive ANTLR group at UEA (currently comprising 7 faculty, 5 postdocs and 8 PhD
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programs. The student will join a well-supported team of chemists and biochemists (Master’s and PhD students, and postdocs) who are well-placed to provide a supportive and ambitious peer group. The student
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. Develop new machine learning methodologies (from artificial neural networks, decision trees, evolutionary algorithms and others) compatible with epidemiology. Produce a digital twin for national suicide and
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-Making and Route Optimisation: Develop adaptive algorithms within a bias-aware ensemble Kalman filter framework to propose alternative flight paths dynamically. The system will aim to maximise safety and