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generalized framework for extremal structural causal models on arbitrary directed acyclic graphs. Our new models will be able to incorporate non-standard extreme directions, which permits the modeling
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formulation. You will build machine learning models linking molecular structure (SMILES descriptors, fingerprints, graph representations) to reaction rate constants, connect molecular dynamics outputs
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components, systems, and the dependencies between them might be captured in a shared structure, and comparing candidate approaches, such as ontologies, knowledge graphs, graph databases, or multi taxonomy
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assurance, data science, data-driven modelling, digital manufacturing workflows, and Digital Product Passport B.3 Hands-on experience in machine learning, ontologies and knowledge graphs, IIoT and digital
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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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mental models, cognitive maps, and cognitive graphs. These approaches have provided important insights into how people perceive locations, learn route layouts, and understand spatial relations. However
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, rooted in digital sciences, mobilizes knowledge engineering, including ontologies and semantic graphs, in order to structure heritage data and model chains of evidence, drawing on standards such as CIDOC
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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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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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PhD Studentship: Bottom-up Decoding of Protein Conformational Landscapes: from Gas-phase to Solution
misfolding-related diseases such as Alzheimer's - but predicting how proteins fold in biological environments remains a key unmet challenge. This project brings together insights from efficient graph-driven