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Field
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computing. Current challenges in quantum technology adoption stem from the lack of standardized benchmarking methods and the inherent difficulty in validating quantum devices beyond classical simulation
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which allows us to control and manipulate the rotational orientation projection states of hydrogen molecules in gas-surface collisions, which can classically be considered to correspond to whether
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Grand Tours, one-day races (monuments and semi-classics), and select World Tour 1-week stage races. The data sources will include video and previous race commentary to ‘code’ key events in races that help
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(e.g. python, R); affinity and experience with performing animal experiments; experience with classical biological methods (e.g. dissections, immunohistochemistry, western blot, PCR, in vitro assays
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energy; thereby minimising farming’s environmental impact. AI machine learning offers a new expedient method of developing control systems for tasks that would be difficult to manage using classical
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optimisation. 2 – Explainable AI with LLMs for Transparent Decision-Making in Design Engineering This project combines classic XAI methods with LLM capabilities to generate human-readable explanations during
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, classical ecological theories and methods to mathematically model species interactions networks and communities across spatial scales. As part of the project, the student will extend these methods and make
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control over generative shape optimisation. 2 – Explainable AI with LLMs for Transparent Decision-Making in Design Engineering This project combines classic XAI methods with LLM capabilities to generate
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The overall aim of the project is to resolve the quantum trust challenge where the inherently quantum nature of these devices, being beyond classical simulation, complicates their straightforward validation
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be placed on curiosity-driven explorations of unconventional light–matter interactions in those systems, and their understanding in the framework of classical and quantum optics, in combination with