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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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and large models, limiting real-world deployment. This PhD focuses on efficient Physical AI, emphasising data-efficient training, reinforcement learning, continual adaptation and edge deployment
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. Successful applicants will, ideally be in post by 2 November 2026. Applicants should normally hold a PhD in a relevant field. We also welcome applications from candidates who are close to completing their PhD
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the popular field of religion and film, incorporating theology, philosophy, study of religion and film studies. The successful candidate may also be able to teach in one or more of the following areas
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of their career. The Career Development Fellowships will enable early career academics to acquire a strong and well-rounded foundation to support future applications for substantive academic roles at Durham
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as computer time, commensurate with career stage. Essential Research Criteria - Associate Professor (Grade 9): 1. Qualifications - a good first degree and a PhD in Physics or a related subject. 2
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and faster digital technologies. We pride ourselves on closely aligning our teaching with the University’s research intensive ethos. Research led learning is embedded at every stage of our undergraduate
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-class or that has world-class potential. Essential Research Criteria Qualifications –- a good first degree and a PhD in Psychology, Neuroscience, Machine Learning, Computer Science or a related subject
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evaluate and embed learnings from the project that will influence T Level policy and curriculum development. The project has workstreams around the promotion of technical careers, T Level placements, and
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the assessment process. Key Responsibilities · Teach modules in appropriate learning environments at undergraduate/postgraduate levels, demonstrating an increasing awareness of different approaches to and