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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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specifically, the PhD student will examine dynamics of sensory processing with a particular focus on the suppression of sensory input around movement initiation. Projects will involve using physiological
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via feedback, interaction and continual learning. It will explore converting human videos, simulation, web-scale data and unstructured experience into supervision and reward signals through relabelling
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industrial data, to transform how pharmaceutical blending processes are designed and scaled. The project combines simulation, high-performance computing and industrially relevant experimentation, providing a
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processes so that our overall energy requirements are much lower, using new technologies, increasing energy efficiency and recovering waste energy. This project will evaluate the potential opportunities
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. Unlike purely theoretical PhDs, your work will directly inform decisions in a live industrial setting. You will be focussed on a live industrial plant, not a hypothetical system, involving direct
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a key challenge in sustainable process engineering: translating microbial electrolysis cell (MEC) technology from lab-scale innovation to robust, industrial deployment. The research will sit at
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industrial scale powder processing routes have been used for mixed oxide (MOx) fuel manufacture, both approaches rely heavily on milling — typically ball milling — to reduce particle size, mix powders, and
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approaches where required. Perform electrical and high-frequency measurements to assess device behaviour, signal characteristics, temporal response, repeatability and device-to-device variability. Acquire and
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mass spectrometry data, RNA sequencing, or imaging. The candidate should also have a background in working with hierarchical data and resolving ambiguous, overlapping signals. Strong programming skills