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include: Intrinsic uncertainty estimation in MLFFs (epistemic & aleatoric uncertainty) Negative log-likelihood and calibration methods for force-field training Feature-space and orbit-based analysis
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
Proposed Start Date 03/16/2026 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public service
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with Bioneer, DTU Bioengineering, DTU Health Tech, and KU, DTU Compute is aiming to create a shared data framework and platform to pioneering models and methods with applications to stems cells
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conditions. 2. Must be able to work at a computer for extended periods for data analysis, and manuscript preparation. 3. May require routine work with biological samples, including recombinant proteins and
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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of 3D+ structure, heterogeneity, and/or dynamics from scattering and/or microscopy data; (2) autonomous analysis and decision making for self-driving and/or human-in-the-loop experiments; (3) computer
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be in charge of finishing ongoing projects in the group, involving the development of probabilistic methods for the identification of non-coding cancer driver elements and of the tumor mode of growth
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Austrian Academy of Sciences, the Austrian Archaeological Institute OeAI | Austria | about 12 hours ago
integrative methods of building documentation and building analysis. Chronologically, the research spans from antiquity until the modern era. For more information of the research activities of the group, see
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. Teamwork: Ability to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications
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Mineral Supply ' project funded by . The aim of this project is to translate research methods pioneered by Durham University Earth Sciences (DU ES) on crystalline basement rock hosted oil reservoirs