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Field
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Excellent communication and writing skills Certifications/Licenses Required Knowledge, Skills, and Abilities Preferred Qualifications Experience handling very large datasets Knowledge of R and Python, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
epidemiologic methods, causal inference, and statistics. Proficiency required in statistical software (e.g., R, SAS, Python). Preferred Qualifications, Competencies, and Experience - Special Physical/Mental
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) • Big data pipelines, distributed computing, and geospatial data processing • Python, R, SQL/NoSQL, containerization (Docker), Kubernetes • API development and web-based analytics tools • Systems
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Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow
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& Pressure Vessel Code, API standards, R5, RCC-MRx, or similar documents. Coding experience in Python. Skilled in oral and written communications, with the ability to present research at all levels
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profile with advanced skills in: - Numerical programming in Fortran, C, or C++, and Python, - Spectral methods using Fast Fourier Transform (FFT), - Code debugging. He should have a keen interest in
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in English, · Scientific publications in Web of Science–indexed journals in the field of cognitive neuroscience, · Programming skills in R, Python, and JavaScript for behavioral modeling, as
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Biology, Data Science, or a related field. Technical Proficiency: Strong command of R. Knowledge of Python is an asset. Multi-Omics Expertise: Proven experience in the analysis and integration of diverse
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Engineering, or a closely related field. * Proficiency in MATLAB or Python. * Prior experience with Neuroimaging analysis (using FSL, AFNI, SPM, etc.) * At least one journal publication as first author
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of Science, Mathematics, Computer Science, or a closely related field. Demonstrated computational expertise (e.g., Python, network analysis, data wrangling). A research profile that engages with questions