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Applications should include: Curriculum Vitae Cover letter detailing your motivation for applying to the advertised research topic and/or project, including how your background, interests, and career goals align with its objectives Transcript of all modules and results from university-level...
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2027 - 04:30 (UTC) Country Luxembourg Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to
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SD-26045-RESEARCHER IN ADVANCED PLASMA-ASSISTED DEPOSITION PROCESS DEVELOPMENT FOR CATALYTIC THIN...
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? In the framework of a bilateral project
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supported by COMMLab, 6GSPACE Lab, HybridNetLab, QCILab, TelecomAI Lab, CSAT Lab, our SW Simulators, and our Facilities. For further information, you may refer to https://www.uni.lu/snt-en/research-groups
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Deadline 15 Mar 2026 - 23:59 (Europe/Luxembourg) Country Luxembourg Type of Contract Temporary Job Status Full-time Hours Per Week 40 Is the job funded through the EU Research Framework Programme? Not funded
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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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detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated active learning and create broad, reliable MLFFs. You will
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embedded in the Doctoral Programme in Complex Systems Science at the University of Luxembourg. The modelling approaches developed in this project share conceptual similarities with adaptive network and
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with the disciplinary approach a very ambitious interdisciplinary research culture has been developed. The faculty’s research and teaching focuses on social, economic, political and educational issues
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effects for drug discovery. The successful candidate will play a leading role in developing gene perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate