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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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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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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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, 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
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engineering, engineering physics, or related field completed within the last 5 years. Experience with Python, C, and/or C++ applied programming. Demonstrated experience in applying and extending CFD codes
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or applying AI/ML approaches to Earth, environmental, ecological, hydrological, or geospatial problems. Experience with programming in Python, GEE, or R, and working with modern scientific computing and/or AI