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Field
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Hands-on experience with machine learning algorithms and developing/testing complex software systems Basic understanding of hardware/embedded software development Additional Requirements (MSCA Eligibility
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analyse and develop new, well-founded methods and learning algorithms that extend the boundaries of existing techniques - for example, with respect to expressivity, generalization, interpretability
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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experimental systems for cryogenic measurements Development of a microwave quantum control & readout stack Development of Python code to operate quantum systems Detailed experimental characterization
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conducted to assess the feasibility of prototypes in terms of activity tracking, updating game elements, and visualisation capabilities. The second objective of the thesis is to develop models and algorithms
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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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collaborators. Tasks include formulating optimisation problems, developing algorithms for optimisation with Bayesian models, and implementing solutions in relevant software. Further tasks include the formulation
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Application deadline: 30/04/2026 Research theme: Nuclear Engineering How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD project is fully funded; home students are eligible to apply
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project’s principal investigator, Associate Professor Lars Rohwedder, an internationally recognized expert in the areas of approximation algorithms and parameterized algorithms, see https://larsrohwedder.com
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co