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Field
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fast-paced research environment and will be responsible for the development of novel research methods, will conduct experiments using in-vitro and in-vivo approaches, will perform project planning, data
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for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins. There will be potential
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Position overview Position title: Postdoctoral Scholar Salary range: See Table 23 for the salary range for this position. A reasonable estimate for this position is $69,073 - $82,836. Percent time
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or related areas must be provided prior to the commencement of employment. Expertise in immunological assays, molecular biology, gene transfer methods, and animal models is required. Certifications/Licenses
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 10 days ago
operators for fire observations for atmospheric data assimilation Implement ensemble methods in the fire emissions estimation and estimate uncertainties Utilize bi-phasic FRP retrievals for dynamic
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, or reading groups), present their work at research methods and field-specific seminars, assist with DDSS projects on occasion, and offer consultation to faculty, graduate students, and postdoctoral researchers
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Hiring Range Proposed Start Date 06/01/2026 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public
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on the development of hybrid QM/MM methodologies, machine learning approaches, and advanced free energy simulation methods. These methods are applied to investigate enzyme catalysis, conformational dynamics
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experiments, etc. Perform data collection and analysis Test, optimize, troubleshoot new and existing methods and techniques, and update protocols Prepare and maintain biological stocks Maintain proper lab and
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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