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engineered for science and research applications (i.e. Slurm, Globus, ESNET Science DMZ, and parallel file systems). Maintains a working knowledge of industry best practices, solution methodologies, and
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without inducing unacceptable hot spots in healthy tissues. Parallel to developing the clinical prototype of the device, it is essential to assess the safety of the therapy by systematically investigating
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productivity while reducing external inputs. In parallel, the lab is expanding efforts to understand microbiome-associated phenotypes that contribute to drought tolerance and soil water retention. This includes
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advanced genome-editing tools. In parallel, we develop in-house sequencing technologies to dissect epigenetic and epitranscriptomic modifications at the molecular level, along with high-throughput platforms
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to run on parallel environments. CUDA and Fortran programming expertise. Experience with HPC job schedulers, especially job submission is required. Slurm scheduler experience. Experience and understanding
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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Demonstrated research experience with HPC, AI/ML and/or distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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Master of Science in Athletic Training and the Bachelor of Science in Allied Health Sciences programs through teaching courses in both programs, serving as a clinical preceptor, and providing athletic