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interpretation (see https://solid-symposium.github.io/2025/ for examples of interdisciplinary research in this space). We seek candidates with a Ph.D. in Linguistics, Computer Science, Cognitive Science, or a
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PhD (or thesis submitted subject to examination) in Earth or Materials Sciences with a focus on rock and mineral deformation processes Breadth of knowledge in physics of materials applied to Earth
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, NRAO, and/or NRL. For best consideration, applications should be received by August 31, 2025. Qualifications Minimum Qualifications: PhD in astronomy, astrophysics, computing science or a related field
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experience with – or the confidence to learn – numerical modeling of planetary atmospheres, thermodynamics, and equilibrium chemistry. Instructions Submit the following materials via Interfolio: 1) A letter of
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Previous Job Job Title Post-Doctoral Associate - Computation (Hanany) Next Job Apply for Job Job ID 369600 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular/Temporary Regular
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Apply now Job no:535789 Work type:Faculty - Other Location:Eugene, OR Categories:Child Development, Research/Scientific/Grants, Psychology Department: Prevention Science Institute Rank: Postdoctoral
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The Department of Population and Public Health Sciences invites applications for a full-time Postdoctoral Scholar Research Associate in clinical and translational addiction research. This position will play a
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
the Renaissance Computing Institute (RENCI), we use data, creativity, and inclusive teaming to revitalize how science is done. Fostering data science expertise and creating cyberinfrastructure isn’t just about
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collaboration with Dr. Suthana and interdisciplinary team members. · Apply advanced statistical and computational approaches to investigate neural dynamics underlying memory consolidation and navigation
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials