31 machine-learning-"https:"-"https:"-"https:"-"https:"-"The-Open-University" 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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Are you passionate about digital learning and ready to shape the future of education in Imperial College London? Join us as a Learning Technologist and take a lead role in the implementation
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technician consisting of experimental chemists, computational chemists, and computer scientists specialising in AI. You are also expected to have the opportunity to engage with events, training and personal
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We are seeking to appoint a postdoctoral research associate in Machine Learning and Chemometrics for Antimicrobial Resistance for 18 months. The successful candidate will carry out research
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applying machine learning to a large and diverse, curated clinical dataset. The candidate should have a PhD or MSc in a relevant field such as Neuroscience, Cardiovascular Science, Computer
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Machine Learning (ML) models to build a chemical ‘rule book’ for small molecule accumulation in bacteria. The position is available full time, starting in February 2026 (or as soon as possible thereafter
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(AI) and Machine Learning (ML) models to build a chemical ‘rule book’ for small molecule accumulation in bacteria. The position is available full time, starting in February 2026 (or as soon as possible
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clinical research groups at the National Heart and Lung Institute at Imperial College London applying statistical, machine learning and simulation approaches to combine experimental and clinical data with