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Ludwig Princeton Branch, Princeton University | Princeton, New Jersey | United States | about 1 month ago
data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Postdoctoral Research Associate - AI/machine learning for
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, numerical method, and data-driven/machine-learning approaches, including: · Numerical simulation of kinetic equations: fast algorithms with stability/convergence analysis; · Data-driven and machine
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machine learning methods to explore how meltwater — at the surface and at the ice sheet bed — is coupled to ice flow. Building on this, you will also examine how meltwater and ice dynamics influence
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Criteria A PhD or equivalent (including industrial) experience in AI/Machine Learning. A record of high-quality publications in the best AI/ML outlets, such as ICML, NeurIPS, AISTATS, JMLR, PAMI, ICLR
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, machine learning and statistical methods as part of a close collaboration between Uppsala University, Westinghouse, and Vattenfall? Would you like to contribute to the development of fast, robust and
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performance utilizing a variety of tools including life-cycle assessment (LCA), machine learning techniques, and/or optimization methods. This position will carry out state of the art research into the
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large‑scale systems neuroscience with machine learning to derive principled models of cortical computation. Our newly refurbished primate facility, state‑of‑the‑art Neuropixels rigs, and high
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Faculty of IT and Design invites applications for a PhD stipend in the field of secure machine learning within the general study programme Electronic and Electrical Engineering; as per November 1, 2026, or
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the complete event reconstruction and systematic uncertainty estimation pipeline for Super-Kamiokande. Reduction of Super-Kamiokande systematic uncertainties using machine learning, control samples
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at the intersection of statistical genetics, systems biology, and machine learning, with strong emphasis on methodological development.
Tasks of the PhD Student
- Develop