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work of Dr Anna Price. This NIHR funded post is available from 1st July 2026 to 31st Dec 2029. The successful applicant will support delivery of the SmartADHD programme: ‘Co-developing a digital
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About the Role This is an exciting position where applicants are invited to join a multi-disciplinary team of bioengineers, biomedical scientists, and computer scientists working together at Queen
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PhD or be near completion, in physics, mathematics, computer science or a related discipline, and possess sufficient specialist knowledge in the discipline to work within established research programmes
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. They will be expected to guide more junior colleagues, including postdocs, research assistants, PhD and project students. The research may involve either analytical work, numerical computations or both
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Bachelor’s degree in Computer Science, Electrical/Electronic/Software Engineering, or a related field. A Master’s or PhD degree in relevant areas will be advantageous. Familiarity with the following areas is
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required to register for a PhD. Background Oxford Brookes University is one of 15 international partners in AGILE a newly established interdisciplinary Marie Sklodowska-Curie Doctoral Training Programme
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About us: This Computational Biologist post forms part of the CRUK City of London Centre and is based within the School of Cancer & Pharmaceutical Sciences at King’s College London. The successful
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computational pipelines suitable for downstream integrative and AI-driven discovery. The role includes training PhD students, contributing to grant applications, project planning, and leading the preparation
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learning frameworks (e.g. PyTorch, TensorFlow) and relevant libraries. Practical experience inscalable data processing, including the use of parallel computing, cloud platforms,and distributed systems
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The Role The role focuses on advancing research in explainable and trustworthy machine learning, with a particular emphasis on mechanistic interpretability and its application to healthcare data. The successful candidate will contribute to understanding how modern machine learning models...