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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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checks with advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). The researcher will use historical QC archives dating back
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that trust frameworks are not only technologically robust but also aligned with societal and ethical expectations. About the Project We are seeking a highly motivated Postdoctoral Researcher to join the
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concepts from nonlinear system identification, optimisation, computational complexity, and data-driven modelling, with the long-term objective of extending these ideas to modern AI and machine learning
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics
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Researcher in The School of Chemistry. The InTeleCat project involves the use of machine learning and AI as applied to organic synthesis. It is a large collaborative project involving researchers in the US
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machine learning aspects of this AI-assisted semantic tagging and fuzzy logic search tool. The Postdoctoral Researcher on the project will work on a project related to an aspect of the hip-hop-specific
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cases) and radiomic features predictive of school-age MRI extracted. Machine learning algorithms of radiomic features predictive of future brain development will be developed. Furthermore, a novel