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self-assembly of ligand building blocks will be generated and explored. Detailed kinetic data gathered during these studies will also contribute to machine learning (ML) approaches in collaboration with
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problems, numerical mathematics, optimisation, machine learning and imaging physics, with applications ranging from medical and industrial imaging to geophysics. For more information, please visit
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meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning
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independently. We value personal development: you will receive training in advanced computational techniques, machine learning, data analysis and scientific communication. You’ll have the opportunity to attend
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looking for a PhD candidate to work on research at the intersection of machine learning, data privacy, and medical imaging. The position is part of a three-year research project that investigates how
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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Engineering or related disciplines, provided you have a strong interest in communication systems and networking. If you have a solid technical background and are excited about future wireless and satellite
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(or equivalent) to model neutron transport and tritium breeding behaviour within breeder blanket configurations. The project will then extend toward accelerated predictive methodologies using machine learning and
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machine learning and AI, probabilistic risk modelling, hydrology and actuarial science. We realize that candidates will usually have expertise in one of these fields and ask for a genuine interest in the
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-trivial software. Solid understanding of algorithms, numerical methods, scientific computing, or machine learning. Ability and motivation to write clean, maintainable and well-tested code. Strong interest