37 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at Imperial College London
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will support the generation of high-throughput mass spectrometry (MS) data to power state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) models to build a chemical ‘rule book’ for
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-edge projects in chemical biology and drug discovery and will underpin the development of state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) models with collaborators. You will
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. Experience solving diffusion or related equations by Monte Carlo methods. Knowledge of MRI/diffusion tensor MRI. Experience with machine learning/deep learning/AI methods. Be part of a team developing cutting
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of people who are dedicated to helping young people innovate through hands-on learning experiences. The workshop has a variety of tools from 3D printers and laser cutters to sewing machines and woodworking
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contribute to the development and enhancement of reporting tools and frameworks, utilising artificial intelligence and machine learning to maximise the use of data, while ensuring that data is accessible
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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
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, ideally applied to computational protein design, molecular modelling, or machine learning for biomolecular systems. Practical experience in molecular cloning, protein expression, purification, and
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(ECRI), you will play a vital role in delivering high‑quality learning, development and support activities that empower Imperial’s early career researchers. Working as part of a collaborative and dynamic