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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 21 days ago
to contribute to innovative projects at the forefront of healthcare delivery improvement, leveraging Large Language Models (LLMs) and clinical data analysis. About the Position Our Postdoctoral Research Program
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Ecosystem Mapping and Modelling Apply for this job See advertisement About the position Applicants are invited for a 4-year position as a postdoctoral researcher in Ecosystem Mapping and Modeling to be based
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the supervision of MSc and PhD students affiliated with SDU Nano Optics as well as in writing grant applications supporting the research at SDU Nano Optics. Further information For further information and details
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22 Oct 2025 Job Information Organisation/Company INRAE Department ECODIV-AgroEcoSystems Research Field Physics Environmental science Researcher Profile First Stage Researcher (R1) Positions Postdoc
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» Biomaterial engineeringTechnology » BiotechnologyTechnology » NanotechnologyYears of Research Experience4 - 10 Additional Information Benefits We offer a post-doctoral position (full-time employment) in
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: 278915095 Position: Postdoctoral Associate: Integrating political economy insights into energy modeling Description: The Peng group in the School of Public and International Affairs and the Andlinger Center
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holds a PhD in Biochemistry, Molecular Biology, Immunology or another relevant Life-Science degree. Expertise in immunology, allergy, FACS (analysis and cell sorting), and mouse models is required
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experimental chemistry, providing a supportive research environment. Applicants should have a PhD in Chemistry or related field, and extensive experience in python programming and machine learning models
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in subcluster management, research data management, conduct independent research, and prepare third-party funding proposals. Required qualification: Eligible candidates must hold an outstanding PhD in
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approach to high-frequency electromagnetic (georadar or controlled-source electromagnetic CSEM) data modelling based on full wave 3D inversion to expand our competence in the characterisation and monitoring