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for fuel system applications. While these methods provide a wealth of knowledge and information, they remain impractical for industrial use. Therefore, AI modelling techniques will be harnessed to develop
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computer simulations by developing fundamentally innovative and advanced protection strategies. To enhance the reliability and safety of low-voltage networks with a high penetration of power-electronic
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geophysical and planetary models. IBM faces several such challenges in practice, many of which are currently approached using Foundation-Model (FM) surrogates. However, certain inverse problems arise in data
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fees. See UKCISA for further information. Starting in October 2026. Later start dates may be possible, please contact Dr Lucia Florescu once the deadline passes. You will need to meet the minimum entry
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criteria: Knowledge of the molecular basis of ageing, muscle physiology, or the circadian clock. Experience or strong interest in data science, machine learning, or bioinformatics. Exposure to high
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Dalton Building at Manchester Metropolitan University. You will develop skills in computational experimentation, financial data analytics, and systemic risk modelling, with extensive opportunities
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should have a strong foundation in artificial intelligence, machine learning, and multi-agent systems, along with experience in programming, data analysis, and model development. Knowledge
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adoption is limited by inconsistent performance data, insufficient understanding of long‑term behaviour, and a lack of standardised testing. This project will investigate new technological pathways
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experimental development at the NQCC. Supervisors: Dr Eran Ginossar and Alexander Owens Entry requirements Open to candidates who pay UK/home rate fees. See UKCISA for further information. Starting in October
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supporting the setup, calibration, and running of laboratory experiments, including the preparation and configuration of sensors, data acquisition systems, and associated hardware and software components