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statistical mechanical theory to analyze the simulation results Basic understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred
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: Experience with CUDA programming; Experience programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high
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tensor network algorithm development and application. Competency with common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel
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) as evidenced by their publications High performance computing (HPC) experience in code development with parallel programming techniques using the message passing interface (MPI) library Proficiency
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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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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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and delay-differential equations. Scientific computing and numerical analysis Large-scale dynamical systems High-performance computing Parallel numerical algorithms GPU computing Strong programming