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reproducible methods to complex population, clinical, and molecular data using R, Python, SAS, or related tools, with well-documented code and analytic workflows. Collaborate and communicate effectively with
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, computational modeling, and data analytics. Experience leading full-scale structural testing. Proficiency in Python or equivalent programming language. Experience with high-performance and high-throughput
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. Major responsibilites: The candidate will work independently to purify recombinant IDR constructs and label them for F19-NMR analysis. In parallel, the candidate will use Python for bioinformatics
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R or Python programming Tissue culture techniques Animal models Interest in translational cancer research, immunotherapy, and neuro-oncology. Other Requirements Ability to work with patient-derived
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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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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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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