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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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numerical solvers (GLPK, HiGHS, CPLEX, Gurobi) for investment planning and operational analysis. Experience with dynamic or co-simulation environments (e.g., combining electric, thermal, and control modules
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state-of-the art methods. Extensive experience in Heterogeneous Catalysis research area as evidenced through peer-reviewed journal publications. Applicants are encouraged to add their three best
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experimental and numerical approaches. Materials classes of interest include components (monomers, polymers, additives, (nano)particles, etc.) utilized in high-performance polymeric materials with relevance in
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society. For more information, please visit our website: https://www.uni.lu/snt-en/research-groups/finatrax/ The person will pursue a Ph.D. degree (Doctorate) in computer science and information system
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skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
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Pathogenesis in the age of the microbiome (MICRO-PATH; https://micro-path.uni.lu ) is a highly competitive, interdisciplinary, research-intensive PhD training programme, supported by the PRIDE
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currently comprising approximately 20 staff members, conducting studies using both qualitative and quantitative methods to investigate youth development and social inequalities. The Centre maintains close
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associated threats. The research project of the PhD student will thus focus on defining methods to track, monitor, and manage the use of GenAI. While this can rely on recentely proposed telemetry framework
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background in ecological/environmental sciences and with a marked interest for fieldwork and for the development and use of innovative methods for the acquisition and processing of field data. How will you