483 parallel-computing-numerical-methods Postdoctoral research jobs in United Kingdom
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or study coordination, or at least active involvement in human studies during your doctoral studies Advanced knowledge in statistics and scientific publishing Experience with methods such as BIA
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of techniques that encompass microbiology, biochemistry, metabonomics, bioinformatics and gut modelling. It will require a skilled postdoctoral scientist who will be familiar with such methods or is
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the work of the ICRC. Each PDRA will also oversee and conduct field research in one of the project’s contemporary case studies. PDRAs will use qualitative methods to conduct fieldwork, and to analyse and
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for our state of the art sport and gym facilities and access to a 24-7 Employee Assistance Programme. • On site nursery is available plus access to holiday camps for children aged 5-16. • Family
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at the Yusuf Hamied Department of Chemistry, University of Cambridge to work on the BBSRC grant "A Platform for Identifying GlycoRNA and Identifying Biases in RNA Pulldown". The role is to develop methods
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of research methods and techniques to work within established research programmes. Applicants will be able to demonstrate experience conducting field-based ecological or environmental science research
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely
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A postdoctoral research associate position is available for a technically strong researcher to join the Oxford Machine Learning in NeuroImaging (OMNI) lab at Oxford’s Department of Computer
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-applicant team the PDRA will lead the day to day running of the project. The project includes four work packages: 1) policy review and workshops with key stakeholders; 2) in-depth mixed method case studies