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: Build reproducible workflows, functions, and R or Python packages; develop visualization tools and open-source applications; and share code through collaborative platforms such as GitHub. Scientific
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and modelers to use the optical constants in the analysis of observational data from, e.g., Hubble, Spitzer, JWST. Candidates are expected to be proficient doing data reduction and analysis using Python
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: Strong data analysis skills, including experience working with large climate datasets. Programming experience, particularly in Python, and proficiency with UNIX platforms. Matlab, IDL and/or GrADS use
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networks Advanced understanding of remote sensing principles Experience working with large remote sensing datasets Strong programing skills with a preference for Python and Julia languages Strong record
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computing skills are required, obtained in a Unix/Linux environment, and ideally including Fortran/C++ coding, as well as analysis tools such as Python, IDL Point of Contact Mikeala Eligibility Requirements
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desirable: Earth, Physical, and/or Computing Sciences, Carbon Cycle Science, mathematical modeling, programming in a compiled language like C or FORTRAN95, scripting with python or R. Location: Goddard
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skills: Experience in statistical programming in languages such as R (ideal) or Python. An understanding of linear models and probability. A background in biology, with a preference for cellular biology
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robotics) for field-based phenotyping Data management and analytics from multi-stream remote sensing platforms Preferred willingness to learn: Use of Python, CRBasic, Matlab, C++, R, or other programming
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by 9/1/2026. Preferred skills: Experience in process-based crop, soil, or hydrological modeling. Proficiency in Python and other programming languages (Fortran and C/C++ are a plus). Familiarity with
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, or unsupervised learning methods. Proficiency in Python and familiarity with scientific computing libraries such as PyTorch, TensorFlow, Pandas, NumPy, and related ML frameworks. Experience with large datasets