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, complemented by publicly available data, to examine transcriptomic dependencies on transcription factor abundance and somatic mutations. These efforts aim to enable pan-cancer subtyping and uncover regulatory
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for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your work as part of our UNT
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facility belonging to SciLifeLab, part of a national infrastructure for research in life science. The creative and research-intense environment contributes to a broad knowledge-base as well as cutting-edge
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Laboratory. This is an opportunity to be part of a team helping with the design of new system software and runtime infrastructure to improve the performance and energy efficiency of future scientific computing
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global issues and making a difference to people's lives. We believe that inspiring our people to do outstanding things at Durham enables Durham people to do outstanding things in the world. Being a part of
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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techniques. Preferred Qualifications: Knowledge of HPC matrix, tensor and graph algorithms. Knowledge of GPU CUDA and HIP programming Knowledge on distributed algorithms using MPI and other frameworks such as
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between models and experiments), Developing empirical force fields Developing scientific software and workflows Experience in programming for HPC environments, including MPI, OpenMP, or CUDA
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++/Python/CUDA programming for real-time image processing. Experience in MRI pulse sequence programming, ideally on Siemens MRI platforms. Experience in MRI image reconstruction; motion and distortion
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tissues in both a membrane bound and secreted form. The secreted mucins are major components of the protective mucus barrier. Even if the mucus clears the majority of invading pathogens, bacteria and