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zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference speed, compute efficiency, and scalability with concurrent agents. Enable real
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on energy-efficient circuit design and software-hardware co-optimization, with exciting applications in graph-based prediction. What we’re looking for: A PhD in Electrical and Computer Engineering or a
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of behavioural models for dynamic analysis. Particular emphasis will be placed on the development of temporal modelling concepts, knowledge graphs, and formal verification techniques, enabling the automatic
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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(directed acyclic graphs, g-computation, propensity score methods, instrumental variables, and natural experiments), counterfactual mediation analysis in its various forms (natural direct and indirect effects
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narratives) Leverage fine-tuned Vision-Language Models (VLMs) for game scenario detection, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting
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Science and Technology Starting date: not earlier than 2 January 2027 and preferably as soon as possible thereafter Project 8: Structured VLMs: panoptic scene graphs for high-level reasoning Employment
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- The Arctic University of Norway, Faculty of Science and Technology Starting date: not earlier than 2 January 2027 and preferably as soon as possible thereafter Project 8: Structured VLMs: panoptic scene graphs