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software that allow time-evolving models of complex systems to be calibrated against sparse, indirect, and uncertain observations. The group develops and maintains an open-source Python framework
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, MCNP, Serpent, OpenMC, and/or MOOSE and familiarity with reactor systems and concepts. Programming/scripting experience including the use of one or more of C/C++, Python, Fortran, Git, and CMake an asset
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for the collection of in-situ mechanical testing data. Knowledge of scientific software in python (numpy, scipy, pandas) or related data analysis suite. Record of productive and creative research as demonstrated by
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completed within the last 5 years or expected to be completed soon. Expertise in AI/ML methods in an environmental or Earth science context Experience programming in Python or related languages A strong
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or CP2K on massively parallel architectures. Solid skills in common scientific programming languages, in particular languages such as FORTRAN, C, C++, and Python Experience in parallel programming with one
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, Python or R Experience in manipulating and analyzing large observation data or model outputs Working knowledge of terrestrial biogeochemistry and nutrient-cycling processes Strong communication skills
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, Python or R Experience in manipulating and analyzing large observation data or model outputs Working knowledge of terrestrial biogeochemistry and nutrient-cycling processes Strong communication skills
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system, MCNP, OpenMC, Serpent, VERA, MOOSE codes, etc.). Experience with common programming and scripting languages (e.g., Python, C++, Matlab, JavaScript). Preferred Qualifications: As this position works
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in programming and simulation tools such as Python, MATLAB/Simulink, and/or ML frameworks (TensorFlow, PyTorch) Experience with physics-based modeling Strong analytical and problem-solving skills, with
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methods Computational or process modeling using tools such as Python, MATLAB, Aspen, COMSOL, Fluent, or OpenFOAM Experience with high performance computing environments Experience working in a classified