254 coding-"https:" "https:" "https:" "https:" "https:" "https:" positions in United Kingdom
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Oxfordshire City Oxford Website https://www.psy.ox.ac.uk https://www.ox.ac.uk/ Street South Parks Road Postal Code OX1 3EL E-Mail hr@psy.ox.ac.uk STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp
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this appointment. Key attributes of the successful applicant include: Strong coding skills (e.g., MATLAB) Advanced statistical experience An excellent publication track record Experience of conducting research with
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1300 and 1900 CE”; https://www.synergy-plague.org/ ), working with Principal Investigator, Professor Philip Slavin (University of Stirling) and Leading Collaborator, Professor Samuel Cohn (University
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and to study on this programme. How To Apply You must apply through the University’s Application Portal: https://applyto.newcastle.ac.uk/ In ‘Course choice’ tab: Type of Study - ‘Postgraduate Research
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and the safety implications arising from these interacting processes. You will use and extend PyBaMM, which is an open-source Python-based battery modelling framework (https://pybamm.org
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on an existing experiment design. This will allow the student to familiarise themselves with the relevant Physics, literature, Fortran code, Python analysis tools, and simulation setup while providing a valuable
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research supervision, during this appointment. Key attributes of the successful applicant include: Strong coding skills (e.g., MATLAB) Advanced statistical experience An excellent publication track record
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bioprocessing and specialised chemicals. Person specification The candidate should have a keen interest in computational work, including coding and software development, numerical optimisation, and machine
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Code. This will include the carrying out of the highest standard of individualised patient care and ensuring that all documentation is accurate and comprehensive. As well as participating in the clinical
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, open code, open educational resources and practices that support replication. Desirable: F1 Experience of estimating uncertainty (such as Aleatoric and Epistemic Uncertainty) in Deep Neural Networks. F2