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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular
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+ 22 + 14 months | Belvaux Are you fascinated by data-driven atomistic simulations for materials science? So are we! Come and join us. We seek a highly motivated and capable PhD candidate to develop and
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the strategy for developing a scalable quantum technology architecture. Our strategy is to establish use integrated colour centres for quantum communication tasks. Key here is to generate spin-photon
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place! The successful candidate will work under the academic supervision from the University of Luxembourg, and in close collaboration with its industrial partner SES, in the development of advanced
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/ How will you contribute? The evaluation by test drivers of new tire designs in real situations is a key part of the tire development process. In this context the role of the tester and the feedback it
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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial
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Science, Life Sciences and Medicine. Through its dual mission of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better
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area of machine learning. This includes conducting literature surveys and establishing state-of-the-art; developing necessary experimental and simulation facilities where required; planning, executing
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The SnT is seeking a Doctoral Researcher to support the research and development work within the SEDAN group (https://www.uni.lu/snt-en/research-groups/sedan ). We seek a candidate with expertise
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-making for an optimal energy system, with specific focus on cost-effectiveness, emission reduction, and social acceptability. D2ET will develop a comprehensive digital platform for planning energy