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and Sobolev-type spaces (with Hytönen and/or Korte), Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game
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text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference
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positions for distinguished professorship. Candidates in areas including, but not limited to, Algebra, Number Theory, Geometry, Topology, Combinatorics, Graph Theory are encouraged to apply. Responsibilities
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and machine learning. Topics of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge
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presentations incorporating graphs, charts, and other graphics; assists in preparing contracts and agreements and Board reports; processes documents consistent with District policy. When assigned to the Peer
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. demonstrated research capability in one or more of the following areas: quantitative modelling of dynamic or adaptive systems reinforcement learning, multi-agent systems, network or graph-based models simulation
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responsibilities. Experience Essential: E1 Experience of analysing human body movement from sensor data (eg RGB videos and/or MOCAP data) using Deep Neural Networks (such as Graph Convolutional Networks). E2
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and compare energy consumption at different scales (building, block, neighborhood, city). 2. Produce relevant analyses and indicators for decision support Generate metrics, maps, graphs and
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Multi-modal Machine Learning-including areas like Neuro-symbolic AI, Knowledge Graphs, Contextual AI, Conversational AI, and Trustworthy & Safe AI. This role also offers the opportunity to explore human
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data. 2. Ability to enter data and update graphs using a computer program. 3. Ability to communicate effectively in both verbal and written form. 4. Ability to remain calm and patient during challenging