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Field
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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their own research program in collaboration with, and in parallel to, Prof. Zanazzi. Penn State hosts a vibrant community of scientists working on many aspects of exoplanetary astrophysics, including
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software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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and tool-using agents for experiment design, simulation steering, data collection, and lab/compute orchestration; planning and memory; multi-agent collaboration. Scientific Reasoning: Program/path