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clusters, biophysics, solids and surfaces, machine learning, quantum computing, and self-consistent fields. We develop new theory and associated computational tools, which are supported and distributed
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interactions in the condensed phase and at surfaces, with a particular emphasis on the development and application of first principles and/or machine learning approaches. Research in the Michaelides group
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, Mendelian randomisation, and other statistical and machine-learning methods. The appointed researcher will have research interests aligned with those of our groups and the freedom to develop their own ideas
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learning methods to improve the understanding, treatment and prevention of human disease. The successful candidate will develop novel statistical and machine learning algorithms to address key challenges in
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a PhD/DPhil or equivalent in a quantitative discipline such as computer science, statistics, machine learning, statistical or population genetics, or a related field. They should have experience in
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We are seeking a full-time Postdoctoral Research Associate in Machine Learning for Grid-Edge Flexibility to join the Power Systems Architecture Lab within the Department of Engineering Science
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projects. It is essential that you hold a PhD/DPhil in a quantitative or computer science related subject (e.g. Statistics, Machine Learning, Biostatistics, AI, Engineering), and have post-qualification
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seek an ambitious and self-motivated postdoctoral researcher with a strong background in bioinformatics and/or computer sciences (or related fields). Experience with AI and Machine Learning models in
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protein design and evolution, using molecular biology and biophysics, along with the latest AI or machine learning tools. According to the development of the project, there may be the chance to learn other
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of materials (e.g., combining structure prediction and machine learning). For example, we work closely with experts in computer science as part of the Leverhulme Research Centre for Functional Materials Design