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Field
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and
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storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems. Co-authorship of peer-reviewed publications
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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
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real-world applications in green chemistry and industrial synthesis. Key Responsibilities: Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic
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that ingest raw on-chain data (blocks, transactions, smart-contract events) from public blockchains into research-grade databases Developing statistical, graph, and/or machine learning models to study
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of Complex Networks, is funded by WASP, the Wallenberg AI, Autonomous Systems, and Software Program. Here, we use systems and graph theoretical tools to describe dynamic behaviors of large-scale interconnected
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following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph
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development experience GTSAM or similar factor graph optimization frameworks Field robotics deployment in challenging environments Multi-sensor calibration and fusion Commitment to open-source development and
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data (unstructured and structured text, knowledge graph, etc) · Design and conduct experiments to test hypotheses with applied statistics · Analyze and interpret results, present research