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experience in nuclear, mechanical, or chemical engineering, engineering physics, or related field completed within the last 5 years. Experience with Python, C, and/or C++ applied programming
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SWAT and MODFLOW. Exceptional Fortran programming expertise is essential and will be a major selection criterion. The selected candidate must be able to read, understand, modify, extend, compile, debug
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computing (HPC), autonomous software infrastructures, and AI-enabled engineering systems. RESEARCH AREAS Area 1 — AI for Programming Systems Research topics include AI-assisted compilation and
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trainee’s curriculum will include gaining independence in developing neuroinformatics workflows and programming to support neuroscientific analyses, clinical research design, fMRI pipeline development
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Biological & Life Sciences Division – Postdoc Researcher: AI for Precision Medicine and Therapeutics
, statistics, machine learning, programming in C, C++, Python Scripting languages and MATLAB, using a parallel computing environment Bioinformatics, network biology, network medicine, network analytics, AI, ML
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to closely collaborate with CS faculty members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience
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Center (HT- RDS Center). The applicant would be responsible for designing and developing workflows for automated, micro-scale parallel experimentation. These workflows will be used for the
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and delay-differential equations. Scientific computing and numerical analysis Large-scale dynamical systems High-performance computing Parallel numerical algorithms GPU computing Strong programming
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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM