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of offers available1Company/InstituteDepartment of Computer ScienceCountryAustriaCityInnsbruckPostal CodeA-6020StreetTechnikerstraße 21 a Contact City Innsbruck Street Technikerstraße 21 a Postal Code A-6020
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University, the Communicable Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global Health Network (MLGH), and wider regional and global partners. Key
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 21 hours ago
) in Geometric Deep Learning to join the Machine Learning and Artificial Intelligence (MLI) g roup to perform research in geometric deep learning for materials science. This research is part of
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the background we imagine would best fit the role. Even if you do not meet all the requirements and feel that you are up for the task, we absolutely want to see your application! The PhD process is a learning
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intelligence, or related areas Experience with large language models, machine learning systems, software testing, program analysis, requirements analysis, or software quality assurance Strong programming skills
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas
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the optical-to-radio wavelength range, from major surveys and space telescopes (e.g: Gaia, SDSS, JWST, Hubble, Roman, Rubin-LSST). These are analysed using advanced machine learning and data-driven methods. My
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of years are investigated by combining fieldwork, lab work and computer simulations. By bringing together our fundamental understanding of system Earth and our fresh curiosity we conduct research that is
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made significant progress in this direction by merging machine learning interatomic potentials (MLIPs) trained on density functional theory (DFT) data, and enhanced sampling techniques to reach the