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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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, 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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/ ). Your tasks will include data collection, analysis, write-up, and dissemination of ongoing and future research. Furthermore, you will play a crucial role in supporting and training PhD students and other
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/ ). Your tasks will include data collection, analysis, write-up, and dissemination of ongoing and future research. Furthermore, you will play a crucial role in supporting and training PhD students and other