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Field
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: Applications accepted all year round Details In this project you will combine state of the art software development approaches for GPU programming with the study of interactions of high-energy particles with
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, software design, and programming models. Ensure long-term maintainability, sustainability and open access by thoroughly documenting projects. Provide support for the use of software libraries, including
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group and develop an innovative and impactful research program, which includes, but is not limited to, population and evolutionary genetics, metagenomics, eDNA-based monitoring, and phylogenomics
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computing systems, GPU programming, machine learning, and/or full-stack. Experience teaching best practices in software engineering. Ability to work with multi-disciplinary teams and to interact cross
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operations of cyberinfrastructure. SDSC supports hundreds of multidisciplinary programs spanning a wide variety of domains, from earth sciences and biology to astrophysics, bioinformatics, and health IT. SDSC
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Mathematics, Physics, Chemistry or related field. Two (2) years of applicable programming experience to include Linux and/or other system administration experience (graduate level student work experience may be
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Mathematics, Physics, Chemistry or related field. Two (2) years of applicable programming experience to include Linux and/or other system administration experience (graduate level student work experience may be
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for scientific discovery, addressing critical societal challenges in biotechnology, clean energy, and emerging technologies. Responsibilities include: Leading a well-funded research program in AI-driven scientific
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for individuals with a passion for AI research, strong programming skills, and a desire to work at the intersection of engineering and scientific discovery. The role will also provide teaching and training
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. The successful candidate will lead an innovative research program in cutting-edge AI computer system technologies. The position requires knowledge of both hardware and software aspects of large-scale AI