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We are looking for a Research Fellow to improve the NEEDLE machine learning code for classifying astrophysical transients and manage spectroscopic follow up. The successful applicant will work with
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-parallelised C/C++ code EXCITON+. Depending on the expertise of the candidates, development of ab initio approaches (e.g., QED coupled cluster) to calculate processes involving molecules in optical cavities can
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of glacier modellers and the continually progressing HiDEM code, and will be embedded in a large team of PDRA ocean and ice modellers within GRAIL. As such, there is scope for the postholder to direct
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involve performing numerical simulations to study magnetic fields in the cores and crusts of neutron stars, using and extending one or more existing codes, but also developing entirely new codes
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-based code optimisation. You will develop novel techniques that harness the power of machine learning to create optimisation strategies for compilers and memory management libraries for next-generation
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-based code optimisation. You will develop novel techniques that harness the power of machine learning to create optimisation strategies for compilers and memory management libraries for next-generation
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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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-thermal evolution in magnetars. The project will involve performing numerical simulations to study magnetic fields in the cores and crusts of neutron stars, using and extending one or more existing codes
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undertaken for the University is performed in the UK. Visa Sponsorship Queries: This role has been assigned an eligible SOC code, and meets the salary requirements for Skilled Worker Sponsorship if full time
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atomic data into our modelling database, development work on our Monte Carlo radiative transfer code, carrying out full simulations at supercomputer facilities, and collaborating on the use