18 modal-analysis-artificial-intelligence Fellowship positions at UNIVERSITY OF SOUTHAMPTON
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statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing to the development of biomarkers and predictive models. A critical part of your
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implementing bioinformatics pipelines from raw data, applying a range of advanced statistical, machine learning, and artificial intelligence (AI) techniques to analyse 'omics and clinical data, and contributing
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large team of scientists interested in the foundational issues of Artificial Intelligence and Deep Learning. For further information about Mathematical Sciences at the University of Southampton please see
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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Sciences are Turing fellows. The Erlangen programme offers excellent opportunities for collaboration with a large team of scientists interested in the foundational issues of Artificial Intelligence and Deep
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We are seeking an outstanding, creative researcher with the skills to develop novel, ‘artificially intelligent’ approaches to the application of nanofabrication techniques – see, for example https
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Astronautics! We are seeking a talented and motivated Research Fellow to contribute to a fully-funded project focused on advancing the computational analysis and manufacturing of these critical materials. Who
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on advancing the computational analysis and manufacturing of these critical materials. Who are we? We are a world-renowned research group at the forefront of aerospace innovation. Our department boasts cutting
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Trust grant with an anticipated start date of the 1st November 2025 or as soon as possible thereafter. The project will involve experiments at Free Electron Laser Facilities and subsequent analysis and
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and child health. They will develop data analysis plans, organise data management, and conduct statistical analysis of national and local routine health datasets, including the Education and Child