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months. This is an opportunity to join The British Open-ended Learning and Discovery (BOLD) Lab, a major new UK Research and Innovation (UKRI) funded initiative that will establish a world-leading AI
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/deep learning-based medical image analysis methods for computerised tomography scans (CT scans). Key applications of such ML/DL methods are illustrated by our prior research (PMIDs 33913675, 33234786
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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution
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seabed sensing research project focused on characterizing the upper-layer geoacoustic properties of deep-water seafloors. The position will support research involving acoustic sensing technologies, data
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this, the postdoctoral researcher will combine machine learning, molecular dynamics simulations and high performance computing (Isambard AI). Applicants must have a PhD in an appropriate area of computational chemistry or
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. Experience with deep learning and programming, preferably in Python, are required and should be evident from your academic track record, including the (online) courses you've followed, your publications
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strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal
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outcome sets. You will either need a PhD (or be nearing completion), in computer science, data science, artificial intelligence/machine learning (AI/ML), health data science, bioinformatics or a related
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studies (GWAS), fine-mapping, colocalisation, polygenic risk scoring, and Mendelian Randomisation; and (ii) deep phenotyping of multi-modal cardiovascular imaging (MRI, CT, echocardiography) from large
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful