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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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high dynamic range (HDR) imaging is redefining the way smartphone cameras and displays capture the world. Despite HDR becoming the new standard, many classic image-processing algorithms and generative
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Research Studentship in Numerical Optimisation and Control 3.5-year D.Phil. studentship Project: Embedded Optimisation for Autonomous Spacecraft Control Supervisors: Prof Paul Goulart The project
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programming skills. Expertise in developing computer vision and machine learning algorithms would be desirable, highly motivated and enthusiastic about advancing AI for societal impact. Qualifications A high
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Supervisors: Prof. Finn Werner – Werner Lab Website Dr. Christopher Waudby – Waudby Lab Website Abstract: RNA polymerases (RNAPs) are essential enzymes for viral replication and represent
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Research Studentship in Neural Engineering 3.5-year D.Phil. studentship Project: Sleep classification for implanted neurostimulation systems Supervisors: Dr. Joram van Rheede, Prof. Timothy Denison
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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adaptation of the mesh during simulation to resolve and track features in the flow. The focus of your PhD would be on developing novel algorithms to efficiently redistribute and rebalance the parallel
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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