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, or a similar field; Strong mathematical background: basic knowledge of graph theory and excellent background in linear algebra, finite fields and rings; Strong background in digital hardware design and
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learnable physical kernels, geometry encodings, and boundary-aware layers; compare to PINNs, U-Nets, graph operators, and transformer baselines. Learning strategy: physics-informed and multi-task losses
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be developing advanced spatial models such as graph-based approaches and network analytics to predict how blue network dynamics, fragmentation and surrounding land use interact to shape ecosystem
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areas, including generative modelling (e.g. diffusion models, flow matching, self-supervised and autoregressive approaches), causal machine learning, graph neural networks, dynamical systems modelling
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The SnT is seeking a Doctoral Researcher to support the research and development work within the SEDAN group (https://www.uni.lu/snt-en/research-groups/sedan ). We seek a candidate with expertise
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, storm activity, and other hazards using graph-based clustering, fuzzy machine learning, and reduced-order models – delivering scientific insight into where and when rerouting is needed. Real-Time Decision
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graphs for heterogeneous pavement engineering knowledge aiming to speed up the learning cycle and support innovation and asset management. Job description The increasing accessibility of data in
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prerequisite. Demonstrated motivation to work on AI-driven modeling of physical and chemical processes, including a genuine interest in graph neural network architectures and interatomic potentials. Experience
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Apply and develop advanced multimodal data tools and knowledge graphs for heterogeneous pavement engineering knowledge aiming to speed up the learning cycle and support innovation and asset
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of research being done. * Ability to set up and operate research instrumentation and equipment. Ability to make accurate assays and to prepare reports, charts, or graphs on results. * Ability to work