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derive an optimal set of items to form a smart self-report instrument. This two-year project is fully funded by the Hearing Industry Research Consortium, and you will be among colleagues working on a
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will then analyse complex patterns of data and derive an optimal set of items to form a smart self-report instrument. This two-year project is fully funded by the Hearing Industry Research Consortium
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high-profile, commercially strategic project that will enable Bublshop to gain an understanding of the integration of passive thermal enclosure system with AI-optimized control strategies
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Applications are invited to undertake a three-year PhD programme in partnership with industry to address key challenges in manufacturing engineering. The successful candidate will be based
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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience