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, machine learning, governance and operations, we aim to address the complex challenges and opportunities of space exploration CfAI is a large research group in the Department of Physics at Durham University
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of reactive force field molecular simulations, supervised machine learning techniques and understanding of mass spectrometry techniques. The post is available for 3 years from 1 September 2026. If you are still
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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(Large Language Models, Convolutional Neural Networks, Machine Learning) for analysis and classification of data.
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towards a shared goal. You will be responsible for the design and pilot testing of machine learning-based automated ultrasound video analysis models that incorporate temporal reasoning. The research will
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machine learning approaches to investigate the mechanisms underlying GC-biased gene conversion and understand how meiotic recombination shapes human genetic variation and genome evolution. Working closely
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, Isomap), manifold learning, and machine-learning classifiers to extract neural geometry metrics from both species. Systematically compare behavioural and neural data across mice and humans, identifying
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publications prior to the panel interview. In addition to further excelling your skills in Computer Vision/Big Data/Machine Learning analyses, this opportunity enables you to: - Work closely with clinicians
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across tissues, over time and upon perturbation for translational benefit, leveraging tissue profiling through multiomic technologies in conjunction with cutting-edge computational and machine learning
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of an extension, subject to funding. You will apply and develop cutting-edge machine learning methods to integrate and analyse multi-omic data to identify disease phenotypes. A key aspect of the role is to bridge