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specialised expertise in the Machine Learning for Engineering sub-theme. Candidates from all areas in machine learning are encouraged to apply, with a special focus on the areas of (i) information theory and
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LAST_RESULT_ENTRY_KEY [x2]Used to track user’s interaction with embedded content. Maximum Storage Duration: SessionType: HTTP Cookie YtIdbMeta#databases [x2]Used to track user’s interaction with
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knowledge of power system security and machine learning being crucial. The Associate will primarily work alongside National Grid engineers to integrate the machine learning backend of the intrusion detection
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EngD: Physics-based AI for Intelligent Machining: Learning, Optimisation, and Uncertainty in Next-Generation CAM Software (sponsored by DigitalCNC) EPSRC Centre for Doctoral Training in Machining
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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Full Time, 37 hours per week Permanent We have an opportunity for a Learning Technologist to join our small, friendly, campus-based E-Learning team. The postholder will work with us to support staff
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We are seeking two full-time Postdoctoral Research Assistants in Machine Learning to join the Foerster Lab for AI Research group at the Department of Engineering Science (central Oxford). The post
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Fixed term for 30 months Reporting to the PI, Prof Erin Saupe, the post holder will be a member of a research group with responsibility for carrying out research for ERC grant ‘Determining the drivers of extinction across space and time’. The post holder will provide guidance to less experienced...
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio