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Field
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Amir Probability and group theoryRandom walks, probability and geometry on groups, harmonic functions, opinion dynamics and other stochastic processes on graphs Gil Ariel Bacterial swarming, collective
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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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rendering, Gaussian splatting, etc.). Knowledge of graph neural networks. Experience in deploying and fine-tuning DL models, also large language or vision-language models. Practical experience on deploying ML
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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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for structural analysis of metabolites and lipids, creating metabolomics knowledge graphs, and inferring fluxes from MS data. The group collaborates with mass spectrometry vendors and technology partners
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methods, knowledge-graph and ontology-based scientific data infrastructures, and agentic workflows for autonomous hypothesis generation, mechanistic exploration, and design of catalytic systems. Candidates
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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