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computationally efficient reduced-order dynamical systems on graph with modern power grid systems as an application. Education and Experience: Applicants must have recently completed a Ph.D. and have exceptional
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learning Broad familiarity with geospatial programming libraries Preferred Knowledge, Skills, and Abilities: Non-LLM foundation model expertise Time Series Foundation Models Expertise with Graph transformers
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, Optimization, and AI • ML/AI for mobility prediction and optimization • Graph algorithms, network science • Spatiotemporal modeling • Operational research for mobility and infrastructure • Real-World Practice
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, or graph-based encodings in materials and molecular AI. Familiarity with frameworks for automated and reproducible workflows. Knowledge of governing regulations around privacy (e.g., HIPAA, ITAR), including
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Veterinary Medicine ● Variant discovery and genome annotation: Apply deep learning and graph-based models to improve variant calling, transcriptome annotation, and functional prediction in veterinary-relevant
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qualitative and quantitative methodologies. Additional desirable technical skills include the ability to use Python, Latex, and a wide range of plotting software, including Excel, Data Graph, Veusz, etc
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Large Language Models (LLMs). The position will also involve creating quantitative evaluation frameworks to assess the quality, realism, and reliability of generated data, as well as integrating graph
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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. May assist in grant and other funding application preparation. Review literature for related research developments and techniques. Prepares written materials, charts and graphs on specialized techniques
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records in hydrology, AI/ML, or water resources engineering. Preferred Qualifications Experience with: LLMs, graph neural networks, transformers, or physics-informed neural networks (PINNs). Cloud computing