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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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assembly, population genetics, statistical genetics, complex trait mapping, and high throughput sequencing genome and programming proficiency in R, Python, Perl, C/C++, Java, and SAS are highly desirable
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a related field. Preferred skills: Experience in AI, machine vision, image analysis, robotics, autonomous systems, and agricultural sensing. Experience with Python, machine learning or deep learning
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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
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or further their skills in using the Large Fire Simulator, FLAMSTAT, the Forest Inventory and Analysis Datamart, the TreeMap dataset, the FuelMap dataset, ArcGIS Pro, R, and/or Python. Learning Objectives
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approaches. Familiar with common scientific programming languages such as python, C++, or Fortran. Experience with high performance computing environments is welcome. Candidates who do not strictly meet these