63 machine-learning-"https:" "https:" "https:" "https:" "U.S" positions at Imperial College London
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methods for predicting the synthesisability of organic molecules Develop and apply informed machine learning methods to chemical problems If appointed at a higher spine point: To assist in the day-to-day
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machine learning model to rule out heart attacks in the emergency room, which has the potential to translate to large savings for healthcare systems in the world, (2) a computational modelling to assist in
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The role will engage in cutting-edge translational research that develops computational models for predicting outcomes in cardiac diseases. This includes a machine learning model to rule out heart
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For details of the appointment and how to apply, please visit https://PLusPortal.PerrettLaver.com quoting reference 8185. Imperial College London is one of the world's great universities, renowned
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. Stefanos Zafeiriou can be found at https://wp.doc.ic.ac.uk/szafeiri/ . Research Associate: A PhD (or be close to completion) in an area pertinent to the subject area, i.e. computer vision, machine learning
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Kogias as part of his ERC Starting project titled CloudNG (https://cordis.europa.eu/project/id/101220079 ). The post will be based in the Department of Computing at Imperial College London at the South
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of Professor Ed Tate at Imperial College London. This role will support the generation of high-throughput mass spectrometry (MS) data to power state-of-the-art Artificial Intelligence (AI) and Machine Learning
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of Professor Ed Tate at Imperial College London. This role will support the generation of high-throughput mass spectrometry (MS) data to power state-of-the-art Artificial Intelligence (AI) and Machine Learning
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in computer security. In particular the work will seek to deliver novel contributions that enable such systems to safely continue to operate even when they have been partially compromised and
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vibrant King’s Road in Chelsea. You will work in the group of Prof Jane Davies (https://www.imperial.ac.uk/people/j.c.davies ), Prof Nick Simmonds (https://www.imperial.ac.uk/people/n.simmonds ) and Clare