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for Computational Sciences. They will collaborate with leading computer and computational scientists at ORNL and external collaborators in the development and application of new computational techniques specifically
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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current processes and identify, propose, and implement improvements to ensure output of high-quality datasets. Work with other team members and leadership to plan project requirements and timelines, shift
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as
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modeling with an understanding of numerical methods and physical processes relevant to ocean circulation and dynamics. Experience with high-performance computing, parallel numerical models, large scientific
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models (rodent handling/surgery experience a plus). ● Familiarity with quantitative/computational approaches (e.g., R, Python, image analysis, statistics) is a plus but not required. ● Ability
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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surrogate models, physics-informed neural networks, uncertainty quantification, or digital-twin development. High-performance computing and parallel numerical workflows. Terms of Appointment This is a full
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trajectory optimization, nonlinear programming, or genetic algorithms. High-performance computing (HPC), parallel numerical workflows, or GPU-accelerated model execution. Terms of Appointment This is a full
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architectural models, system-level simulators, and performance modeling frameworks for QHPC systems, capturing relevant characteristics of quantum processing units, classical HPC resources, interconnects, system