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ready. Integrate grid-edge measurements and system-level observations into unified workflows for DER aggregation and parameter identification. Apply machine learning techniques to improve DER/IBR model
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as
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will design and implement differential privacy solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing
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, retrieval-augmented generation systems, or other AI-enabled tools that improve access to structured or unstructured data. Experience with machine learning, artificial intelligence, and/or natural
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a focus on multimodal learning, computer vision, and scientific machine learning Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex data, and
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, embeddings, dimensionality reduction, anomaly detection) Physics informed machine learning Practical experience using ML models: Dataset curation and preprocessing Applications leveraging off-the-shelf ML
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, computer engineering, computer science, or a closely related discipline. Working knowledge of machine learning and deep learning models, including their application within manufacturing environments
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. Engage with the broader community for computational methods, artificial intelligence and machine learning, and real-world coupled physics applications. Deliver on ORNL’s mission by aligning behaviors
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a related discipline A minimum of 4 years of applied experience Hands-on experience with training machine learning models on high performance computing infrastructures leveraging GPU accelerators