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Field
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biological domains is a must PhD in computational biology, computer engineering, computer science, (bio)statistics, artificial intelligence, physics, or related. Desire to push the frontier
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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Manifold learning, shape space analysis, machine learning, mathematics of data scienceMathematical modeling and manifold learning of high-dimensional data geometry, with theoretical foundations and
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framework for the joint analysis of large-scale structure (LSS) and gamma-ray data. By combining these complementary probes of the same underlying matter distribution, the project aims to sharpen constraints
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analyses. The postdoc will be hosted at TDB, co-supervised by both groups, and will work at the interface of scientific computing, machine learning and particle physics. Project description Searches for dark
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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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of the world’s largest research environments in computational science, with large activities in areas such as machine learning, optimization, scientific software development and high-performance computing
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successful in this role, you will hold (or be close to completing) a PhD/DPhil in machine learning, artificial intelligence, computer science, epidemiology, health data science, or a related quantitative
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against simpler machine-learning baselines; • train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies and
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and