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physical). Solid background in programming and experience with machine learning. Knowledge of participatory design and co-creation methodologies. Ability to learn independently and passion for research
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triggered by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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physical models. As the PhD researcher on this project, you will work at the intersection of machine learning, geometry processing and industrial simulation. You will have the opportunity to explore
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North Carolina Agricultural and Technical State University | Greensboro, North Carolina | United States | 2 days ago
and research in mathematics, statistics, or data science; demonstrate a strong commitment to integrating learning, discovery, and engagement; and possess extensive higher education experience, including
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Researcher (R1) Positions PhD Positions Application Deadline 15 Feb 2026 - 23:59 (UTC) Country Spain Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU
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LevelPhD or equivalent Skills/Qualifications We are seeking highly motivated candidates with one of the following profiles: Applied Mathematics, Signal Processing, or Computer Vision: A PhD in these fields
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eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary Outstanding applicants are desired for a Part-Time Lecturer (PTL) teaching position as Co-Course Director for a
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applications for postdoctoral or more senior research associate positions broadly in statistics and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year
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and free-energy calculations in explicit solvent. The postdoctoral researcher will employ machine-learning-accelerated methods throughout the workflow, contribute to the development of new computational