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, or academic projects • Experience in writing research code in collaborative settings using languages such as R and Python • Ability to work effectively as part of a multi-disciplinary research team • Strong
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, climate economics, operations research, or statistics); • Proficiency in at least one scientific programming language (e.g. Julia, Python, etc) required; • Experience with numerical models (preferably
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, including proficiency in programming (R and/or Python) Demonstrated expertise in one or more relevant areas, with the ability and interest to develop new approaches at the intersection of methods development
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, biological sciences, computer science, machine learning) Demonstrated quantitative skills, including proficiency in programming (R and/or Python) Previous experience developing statistical methods or working
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for advanced data analysis and/or experimental control (e.g., Python, SPEC, MATLAB, etc.) Experience with relevant sample preparation and lab-based analyses (e.g., SEM, Raman) Experience with or interest in
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languages like Python or C, and or developing and/or using computational methods for analyzing large datasets. Demonstrated experience in developing computational algorithms for solving problems, preferably
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environmental impacts (e.g., greenhouse gas emissions and impacts to air and water quality). Interest in spatially explicit environmental impact assessments at regional and local scales. Proficiency in R, Python
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studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit
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Engagement with Industry Partners and Scientific Community – 10% Requirements Ph.D. in Atmospheric Sciences, Climate Science, or related field. Proficiency in scientific programming (Python, R, MATLAB
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– 10% Requirements Ph.D. in Atmospheric Sciences, Climate Science, or related field. Proficiency in scientific programming (Python, R, MATLAB, or equivalent) is required. Experience with atmospheric