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We are looking for a creative and enthusiastic Research Assistant for a role focusing on Clinical Machine Learning and Natural Language Processing. You will be working on an exciting project, funded
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of power and propulsion systems, heat and mass transfer, thermo-fluid systems simulation and programming, as well as exposure to software platforms and algorithms for Artificial Intelligence, Machine
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(Technische Universität Berlin) are leading experts at the interface of machine learning and imaging science; Dr Breen (SKA Observatory), Dr Elosegui (MIT Haystack Observatory), and Dr van Heeswijk (Lausanne
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, digital health outcomes and wearable technology advanced analytics. This post will involve collecting wearable technology data, implementing novel machine learning advanced analytics for processing and
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operational practices • Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems • Developing machine learning models to accelerate mixed-integer
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transfer, thermo-fluid systems simulation and programming, as well as exposure to software platforms and algorithms for Artificial Intelligence, Machine Learning and Data Analytics. You will have experience
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functional genomics, bioinformatics, and machine learning to support the generation, interpretation, and screening of large-scale experimental and computational outputs. The successful candidate will
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science, engineering, or a related discipline, with significant postdoctoral research experience. The ideal candidate will have strong expertise in computational biology, machine learning, and quantitative analysis
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machine-learning-based approaches, and to evaluate the thermodynamic costs of quantum operations. You should work effectively as part of a team and engage constructively with collaborators. You will hold a
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programme focused on applying AI‑driven analysis to in situ advanced microscopy of fibre‑based materials. The project aims to develop and deploy machine learning tools that extract real‑time structural and