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transcripts should be sent to Prof. Zhirong Liao. Suitable applicants will be interviewed, and if successful, invited to make a formal application. Do not submit your application via the My Nottingham platform
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and academic transcripts should be sent to Prof Dragos Axinte. Suitable applicants will be interviewed, and if successful, invited to make a formal application. Do not submit your application via the My
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Supervisors: Prof. Gabriele Sosso, Dr Lukasz Figiel, Prof. James Kermode Project Partner: AWE-NST This project utilises advancing machine learning techniques for simulating gas transport in
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date: 29 September 2025 Award duration: 4 years Application closing date: Friday 2nd May 2025 or until the position is filled whichever is the soonest. Sponsor: EPSRC & HR Wallingford Supervisors: Prof
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autonomous by embedding machine learning algorithms to search through different reaction parameters Person Specification Candidates should have been awarded, or expect to achieve, EITHER: A Bachelors degree in
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considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++). An interest in physiological signals. Home Student
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Supervisory Team: Prof Middleton, Prof Altamirano PhD Supervisor: Matt Middleton Project description: Black holes grow by accreting material through a disc which is bright across the EM spectrum
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should operate in an AI context. The AI revolution has sparked ongoing debates that highlight the multifaceted role of AI and algorithms in shaping our world—in ways that engage deeply with law. Fully
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the