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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
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is modelling and optimization using molecular dynamics and machine learning. The position is part of a team looking at these fascinating systems from their fundamental properties and making them in
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feature filtering procedure to deal with the large feature set necessary to predict the thermoelectric ZT of a material. - Improve the already existing experimental dataset. - Apply different machine
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conceptual DFT (linear response function, Fukui functions) or QTAIM theory (delocalization index), and their validation on a set of compounds known from the literature - interfacing a MLIP (Machine-Learned
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Home EMA The European Magnetism Association Executive Board General Council Documents Membership EMA news Communication Social Networks Mailing Event Dissemination Rules All news EMA editorials Obituaries Awards beyond EMA Materials 2023 survey Commitments Young EMA EMA Awards Technical...
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] Subject Area: Statistics and Machine Learning Appl Deadline: 2025/12/01 11:59PM (posted 2025/09/04, listed until 2026/02/01) Position Description: Apply Position Description The Department of Mathematics
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, computational quantum many-body physics, and machine learning The Quantum AI lab at ETH (Prof. Juan Carrasquilla ) invites applications for postdoctoral positions to work at the intersection of computational
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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and
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and methods for advancing the research effort Design and carry out computer experiments on deep learning and related robotic simulations Collaborate with other engineers to create prototypes of embodied
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Are you excited about advancing the next generation of engineering education? Do you want to investigate how student teams navigate the socio-emotional aspects of learning while solving complex