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Field
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system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research
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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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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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(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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environments Optimize scene graphs, memory management, asset streaming, and runtime performance Contribute to research proposals and peer-reviewed publications Generative AI Integration Generative scene
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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