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future colleague? Apply now! Education •PhD in materials science, physics or similar. Experience and Skills •Excellent experimentalist, possible to work on an interdisciplinary project linking fabrication
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The PhD position is embedded within the MICRO-PATH Doctoral Training Programme, funded by the Luxembourg National Research Fund. MICRO-PATH, or Pathogenesis in the Age of the Microbiome (https
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job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Temporary contract | 14
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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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, particularly in MATLAB or Python, would be beneficial. Your profile The candidate should possess (or be in the process of completing) an MSc degree or equivalent in Electrical/Electronic Engineering, Computer
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Application Deadline 12 Nov 2026 - 07:52 (UTC) 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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group with broad research competences and interests, with expertise covering mathematics, engineering, computer science and social sciences. We offer excellent working conditions in an international and
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Luxembourg. The candidate will focus on fabricating copper/carbon composites for various applications, including lithium-ion batteries. LIST has developed a water-based process that uses functionalized carbon
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Application Deadline 14 Nov 2026 - 03:47 (UTC) 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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the impacts of various anonymization techniques from a business, legal and regulatory standpoint Designing and evaluating a reference process model to guide the implementation of legally compliant data