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Applications are invited for a doctoral position at the Institute for Lifespan Development, Family and Culture at the University of Luxembourg. The position is part of an international research
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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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. Finally, the research will develop efficient algorithms and test them on realistic networks and using real data from energy and public transport operators. The Doctoral student is also expected
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research focused on biomedical image computing. Our work involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim
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https://www.uni.lu/snt-en/research-groups/trux/ . The successful candidate will: Conduct cutting-edge research in multimodal and multilingual natural language processing Develop and curate multimodal
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our website: https://www.list.lu/ How will you contribute? This is a PhD project to investigate the role of evaporation during small rainfall events on forest function. Low-volume water pathways in
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. The group consists of doctoral and post-doctoral researchers from diverse backgrounds. For more information, please visit our website: https://wwwen.uni.lu/snt/research/finatrax/projects Successful candidate
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citizens in their choices, public authorities in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute
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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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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