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Website https://www.timeshighereducation.com/unijobs/listing/404686/postdoctoral-associ… Requirements Additional Information Work Location(s) Number of offers available1Company/InstituteNEW YORK UNIVERSITY
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supermassive black holes through a combination of observational data, machine learning techniques, and cosmological simulations. The group is actively involved in multiple JWST Guaranteed Time Observation (GTO
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neuroscience. The position is open under specific projects as well as general research involving the application of methods from theoretical physics, mathematics, and machine learning with the goal to understand
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opportunity to align with the most relevant academic department in the College of Architecture, Art, and Planning and teach one course per year subject to department needs. The Postdoctoral Associate will be a
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, or Stata); · Creating and managing very large datasets; · Machine learning skills. Basic Qualifications A Ph.D. in any business discipline, organizational behavior, economics, statistics, environmental
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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https://www.shih.hms.harvard.edu/ . What you’ll do: Develop DNA-based sensors that seed crisscross assembly of single
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the world, we work together to develop solutions for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2025/postdoctoral-fellowship-on-int
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation