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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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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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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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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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into this material and support tailoring its properties. For this, you will: Contribute to method development for ultra-fast MLIPs (Xie et al., npj Comput. Mater., 2023) Develop realistic MD simulation protocols
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strong background in textiles and understanding of textile functionalization methods. You have excellent communication, collaboration, and interpersonal skills and fluency in English (written and spoken
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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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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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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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) - Three-dimensional conformally flat Lorentzian manifolds through experimentation (Karin Melnick) - Representation-theoretic methods in algebraic geometry (Karin Melnick & Pieter Belmans) - Computational