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of planetary atmospheres and surface environments · Strong programming skills (e.g., Python, C/C++, or Fortran) and familiarity with high-performance computing, and · Excellent organizational and written and
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, spatiotemporal modeling, high-dimensional statistics. ● Proficiency in statistical programming (R and/or Python) and good practices for reproducible research. ● Experience working with large datasets and cloud
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, representation learning) Spatiotemporal modeling or geospatial/temporal data analysis Medium-to-Large-scale foundation models pretraining/fine-tuning paradigms Strong programming skills in Python and experience
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candidates are expected to be skillful in some, but not necessarily all, of the following areas: experience with software languages and tools such as Python, R, Linux, GitHub, Globus, high-performance
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. Solid state physics; thermodynamics and kinetics; Python coding and machine learning skills. Additional Application Materials Required: Special Instructions to the Applicants: The Ph.D. must be conferred
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working with large biological datasets and statistical software (R, Prism, Python, or equivalent). · Prior experience in gut-brain axis research, neuroimmune signaling, chronic pain, opioid biology
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python and using plant root segmentation software Experience segmenting 3D CT images in plant sciences Experience with high performance computing/high throughput computing resources for research Evidence
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or Python Experience working in high-performance computing environments Familiarity with version control systems such as Git or GitHub Demonstrated scientific writing and publication experience Interest in
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analysis and signal processing Experience working with pediatric/child populations in research settings Familiarity with bilingualism or language/literacy research Experience with MATLAB, Python, or R
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analytics projects as needed (e.g., ticketing, fan engagement, and scheduling). Build and maintain data pipelines and models using R, Python, SQL, and cloud computing resources, following best practices