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16 Jan 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Educational sciences » Learning studies Engineering » Systems engineering Researcher Profile
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design
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The logic and learning (LOL) group is recruiting a postdoc funded by ELLIS institute Finland. You will work with Associate Professor Andrew Cropper . We work on combining logical reasoning and
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knowledge and advanced transfer learning techniques. The methodology incorporates fundamental radar wave propagation equations into the diffusion process, allowing for more accurate and physically consistent
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these images. This project proposes an innovative approach that combines state-of-the-art diffusion models with physical radar knowledge and advanced transfer learning techniques. The methodology incorporates
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04.02.2026, Academic staff The Chair of Human-Centered Technologies for Learning at the Technical University of Munich (TUM) offers exciting Ph.D. and Postdoc opportunities in the cutting-edge field
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 2 months ago
computational mechanics and scientific machine learning. The successful candidate will work on the design of hybrid, physics-informed modeling and identification frameworks for complex dissipative material