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Field
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Requirements REQUIRED: Ph.D. in Atmospheric science, Civil and Environmental engineering, mechanical engineering, or a related field; knowledge of and demonstrated skillset in physics-informed machine learning
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Posting Open Date Posting Close Date Qualifications Minimum Education and Experience The candidate should hold a PhD degree in Computer Science, Information Systems, Computer Engineering, or a related field
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. Implement and optimize data representations and pipelines suitable for machine learning and uncertainty quantification. Collaborate with AI/ML experts to design and test inference methods that map
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to effective therapeutic strategies targeting IDPs Collaborate on the development of open-source machine learning tools to support these therapeutic designs Work closely with high-throughput screening teams
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findings to military stakeholders and the implementation of evidence-based practices within military medical populations. Therefore, applicants should have experience working with, or significant interest in
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undergraduate researchers assisting with the project. The Post Doctoral Researcher could have the opportunity to learn fossil preparation and conduct collection, if needed to augment analysis and writing
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to learn fossil preparation and conduct collection, if needed to augment analysis and writing. The Position is classified as a Teaching Post-Doctoral, and they may teach courses as an Instructor of Record
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
machine learning methods like machine learning interatomic potentials is a preferred qualification. This preferred start date is January 2026 and can be negotiated. The initial appointment is for one year
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join our multi-disciplinary team of researchers, including immunologists, PhD students, physician-scientists, and bioinformaticians. The postdoctoral fellow will work in an NIH-funded laboratory and will
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD degree (or foreign equivalent) in Electrical Engineering or a closely related field in hand by