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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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researchers, molecular biologists and computational microbiologists. Our work is funded by the Ellison Institute of Technology, Oxford Ltd and the pipelines we develop are deployed in https://www.eit
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researchers, molecular biologists and computational microbiologists. Our work is funded by the Ellison Institute of Technology, Oxford Ltd and the pipelines we develop are deployed in https://www.eit
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will have a PhD in a relevant scientific discipline and sufficient specialist knowledge relevant to the project to be able to make a start on day one – we do not expect everyone to have all the skills
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computational cognitive neuroscience/psychology, computer science, or relevant quantitative field, and a demonstrably strong track record of scientific publication and novel idea generation. You will have strong
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://baldwinlab.chem.ox.ac.uk/. About you Applicants must hold a PhD Biophysics, Chemistry or a related area, or be close to completion, prior to taking up your appointment. The research requires experience in NMR, familiarity
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PhD/DPhil in Chemistry, Biochemistry, Immunology, Molecular Biology, or a related field, with hands-on experience operating and maintaining Bruker and/or Thermo LC-MS/MS platforms. Proficiency in data
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the delivery of the departmental masters programme. The successful applicant will have a relevant PhD or DPhil with post-qualification teaching and research experience, sufficient depth and breadth
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background in (computational) physics or another exact science at an academic level • A background in software engineering • Aptitude for understanding and analysing scientific concepts • Proeficiency in
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experience in working with human neuronal cell lines that provide models of understanding the biology of protein aggregation and its phenotypes. About you Applicants must hold a PhD in Chemistry or a relevant