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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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, pharmacology, genomics and multi-omics, as well as growing methods in advanced analytics of health data e.g. machine learning to improve human health with a focus on therapeutics. These posts will work alongside
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Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating catalytic reaction mechanisms, with a focus
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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
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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September 2027, with some flexibility upon agreement. - A PhD in Mathematics (completed by the start date) is required. Profile We welcome applications from highly motivated candidates with a background in
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project TARGETWISE. The candidate will be responsible for conducting machine learning omics data analysis within the computational team. The details on responsibilities, obligations and rights
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técnicas de machine learning e IA. Sólidos conocimientos en análisis y tratamiento de datos, programación y desarrollo de modelos analíticos. Experiencia con bases de datos y herramientas de análisis
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of reactive force field molecular simulations, supervised machine learning techniques and understanding of mass spectrometry techniques. The post is available for 3 years from 1 September 2026. If you are still
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microscopy. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master