27 machine-learning "https:" "https:" "https:" "https:" "U.S" positions at ICN2 in Spain
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areas of nanoscience and nanotechnology. Job Title: Research Assistant in Active learning Research area or group: Theoretical and Computational Nanoscience Group Description of Group/Project: In
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well as regional efforts such as the Quantum Catalan Academy (https://cataloniaquantum.eu/ ) and the Master in Quantum Science and Technology (https://quantummasterbarcelona.eu/ ). These initiatives contribute to a
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. Estimated Incorporation date: as soon as possible Action funded with CEX2021-001214-S/MICIU/AEI/10.13039/501100011033. How to apply: All applications must be made via the ICN2 website https://jobs.icn2.cat
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at the 'Group of Smart Nanoengineered Materials, Nanomechanics and Nanomagnetism -Gnm3' (https://jsort-icrea.uab.cat/) of the Universitat Autònoma de Barcelona (UAB). The position is in the framework
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collaboration of 14 European research institutions and is funded (https://cordis.europa.eu/project/id/101136269) by the European High Performance Computing Joint Undertaking as part of the Horizon Europe program
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APPLICATION AREAS OF RESEARCH Health Energy Environment Information, communication and quantum technologies Candidates can review the research lines here: http://icn2.cat/en/research During the application
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to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and electronic
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parameters affect material properties and functional performance, and interacting with machine-learning and modelling teams to translate experimental results into predictive datasets. Preparing reproducible
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to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials. · Other research experience will be considered
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valued. · Knowledge of chemical reactions and how to model them through computer simulations is highly valued. · Knowledge of classical molecular dynamics, including Machine Learning Interatomic Potentials