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to scientific computing and other scientific domains. Candidates with strong backgrounds in stochastic analysis, numerical analysis, sampling methods, optimization, scientific computing, or related areas
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
$62,000- $70,000 Proposed Start Date Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public service
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Environmental Science and Technology Consortium) project, supporting the operation, evaluation, and development of AIRPACT (https://airpact.wsu.edu/), the regional air quality forecast system that provides daily
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) Laboratory. The group develops clinically grounded artificial-intelligence and data-science methods to derive scalable digital biomarkers from echocardiography, cardiac computed tomography, electrocardiography
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Link https://www.ubjobs.buffalo.edu/postings/63869 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment Term Salary Grade E.89 Posting Detail Information Position
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, including appropriate controls, statistical approaches, and methods to ensure reproducibility and scientific rigor. · Independently interpret experimental results, troubleshoot technical and
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research using mixed methods, qualitative and quantitative research designs including interviews, focus groups, surveys, and experiments, analyzing quantitative and qualitative data, publishing in leading
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, etc.)? Technical Merit and Relevance to Marine Energy (30%) Is the proposed research a viable concept or topic relevant to marine energy? Are the methods and approach for the proposed research
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computational methods and collaboration, HELM addresses global challenges by blending eco-social health determinants, human-natural system interactions, and convergence research. The selected candidate will work
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physics-informed and physics-ML hybrid approaches that integrate domain knowledge with data-driven methods to advance hydrological process understanding and prediction. Conduct multimodal, multiscale data