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interdisciplinary team spanning statistics, machine learning, genetics, and population health. You will work closely with collaborators at the Nuffield Department of Population Health (NDPH), the Big Data Institute
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electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
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reports and grant proposals. You should possess a PhD or DPhil (or near completion of) in Machine Learning or Maths. Informal enquiries may be addressed to jakob@robots.ox.ac.uk For more information about
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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, and machine learning. The environment at GBI will allow researchers to undertake ambitious, long-term, collaborative research, and we will actively support the translation of research to commercial
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We are looking for a Postdoctoral Research Associate reporting to the Principal Investigator Prof Yee-Whye Teh, they will be a member of the Oxford Computational Statistics and Machine Learning
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operational practices • Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems • Developing machine learning models to accelerate mixed-integer
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to completion) or possess equivalent research experience in a relevant computational field such as data science, artificial intelligence, machine learning, computer science or statistics. They will bring strong
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of applying them to data. Collaborative endeavours with members of the IPMU and Oxford groups is highly encouraged. You will have the opportunity to teach. Applicants should have a PhD (or close to completion
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task essential for grid stability. The goals of the project are twofold. The first goal is to accelerate the solution of the large mixed-integer optimisation problems required to balance energy. The