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- Delft University of Technology (TU Delft); yesterday published
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distresses and causality. The ultimate goal of such a knowledge representation is to create a future-proof framework that enables FAIR data sharing within initiatives such as Knowledge-Based Pavement
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requires systematic data handling to establish a clear link between distresses and causality. The ultimate goal of such a knowledge representation is to create a future-proof framework that enables FAIR data
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engine). This position is ideal for someone interested in natural language processing, geographic information and knowledge representation. It is part of the ERC-funded project GeoTrAnsQData, which
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interested in natural language processing, geographic information and knowledge representation. It is part of the ERC-funded project GeoTrAnsQData, which develops the foundations of a transformative GeoQA
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information; Logic, constraint solving and satisfiability (SAT, #SAT, SMT); Knowledge representation and reasoning (decision diagrams, tensor networks, DNNF); Assist in relevant teaching activities. Where you
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-modal representation learning, and self-supervised learning for this novel perception task. The developed models should provide holistic representations of all surrounding traffic by fusing multi
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. These representations justify or hide the often violent [neo-]colonial, extractive and technocratic practices that deserts have been and are subject to, dominating and disrupting the ecological and cultural delicacies
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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evaluate feature engineering and data representation strategies for heterogeneous datasets obtained from material synthesis, characterization, and functional testing. Apply uncertainty-aware modeling, active
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to support policymakers in designing effective climate strategies. In this PhD project, you will work on improving the representation of feedbacks, tipping points, and extreme events in the MIMOSA model, a