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processes and circularity will be a bonus. Process system engineering models (e.g. superstructure model, material flow model, P graph, process simulation, etc.) Data-driven modelling and/or artificial
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for that month). 2.5. Tasks to be carried out: Development of theoretical approaches in the field of inductive spectral theory and information theory applied to transformer- and graph neural network-based deep
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qualification. Professional assignment: Chair of Scalable Software Architectures for Data Analytics (Prof. Dr. Michael Färber) Research areas: Natural Language Processing, Large Language Models, Knowledge Graphs
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Practical experience in working with MATLAB/Python/R Knowledge of Graph Neural Networks, Transformers, or probabilistic modeling Experience with distributed or edge AI systems We offer: 4-year PhD position in
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modelling/AI is highly desirable. Additional experience of recycling processes and circularity will be a bonus. Process system engineering models (e.g. superstructure model, material flow model, P graph
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human activities that push species to extinction and potentially disrupts ecosystem functionality. Our interdisciplinary lab will develop novel Graph Representation Learning models to understand and
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given the increased pressure from human activities that push species to extinction and potentially disrupts ecosystem functionality. Our interdisciplinary lab will develop novel Graph Representation
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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FAIR/open-science practices, the BrainGlobe ecosystem, EBRAINS, NIH BRAIN Initiative resources, and Brain Maps 4.0 or analogous mesoscale chemoarchitectural atlases. Additional familiarity with graph
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relevant to physics, such as CNNs for image-based field prediction, Graph Neural Networks (GNNs), or Physics-Informed Neural Networks (PINNs) Solid grasp of numerical methods, partial differential equations