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data gaps by combining process simulation (e.g., Aspen software) with machine learning techniques. By developing accurate, large-scale life cycle inventory data using enhanced digital tools like deep
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undergraduate degree (or international equivalent) in computer science, or a related discipline such as physics, mathematics, or engineering. Preferred qualifications are a 4 year undergraduate and/or a Master's
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meeting minutes. Identifying and implementing process improvements, streamlining workflows and increasing efficiency. Implementing and facilitating agile methodologies, including sprints, stand-ups, and
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). • Eligibility: First degree and Masters in one of engineering and computing fields • Standard departmental requirements: First Class • Experience in physical modelling and machine learning, interest in medical
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Exciting Fully Funded PhD: Computational Modelling for High-Pressure, Low-Carbon Storage Technologies. Be a Key Player in Shaping the Future of Clean Energy Storage! School of Mechanical, Aerospace
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Finding and measuring growing black holes in the next generation of astronomical surveys School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr J Mullaney, Dr S Littlefair
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AMRC marketing teams Programme Administration & Support Be the point of contact for students and supervisors, and provide guidance and support on matters related to the PhD and EngD programmes Support
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expectation to contribute to scientific publications and demonstrations. Support will be provided by senior colleagues in the Digital Manufacturing Laboratory. You will have completed a First degree in Computer
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High-speed astrophysics with ULTRACAM and HiPERCAM School of Mathematical and Physical Sciences PhD Research Project Self Funded Prof V Dhillon, Dr S Littlefair Application Deadline: 31 May 2025
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to accelerate the process of scientific discovery. They have shown promise in chemical and materials science where they can explore vast multi-dimensional parameter spaces which would otherwise be impossible