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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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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: This PhD is aligned with an exciting new multi-centre research programme on parallel mesh generation for advancing cutting-edge high
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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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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis