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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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development, programming (Python, Julia, C++, Fortran, etc), and excellent oral and written communication skills are required. Additional qualifications The ideal candidate has experience in developing
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comfortable working with optics, fluidics, detectors, light sources or related laboratory hardware. You are interested in instrument automation and quantitative data analysis. Experience with Python, LabVIEW
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 17 days ago
degree in a relevant STEM field, including Meteorology, Physics, Mathematics, or Computer Science. -Advanced programming skills with experience handling and analyzing big data (e.g., Python). -Previous
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assembly, annotation and phylogenetic or pangenomic analysis, and be confident working in Linux and high-performance computing environments using R, Python, or equivalent tools. You will also need a strong
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programming/scripting (Bash,Python) Preferred Qualifications The candidate should have demonstrated experience in molecular modelling, particularly in the areas of biophysics or material design (all atom
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record. Preferred Prior experience with spintronic materials or beyond-CMOS device platforms. Familiarity with MATLAB or the Python scientific stack. Prior involvement in multi-PI collaborations
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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, proteomics or whole-genome sequencing data, is essential, along with strong quantitative and computational skills. Experience using R, Python or other statistical programming languages to analyse biological