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foundation in machine learning, deep learning, or computer vision Proficiency in Python and experience with ML frameworks such as PyTorch or TensorFlow Demonstrated research productivity (e.g., peer-reviewed
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, electrical engineering, mechatronics, robotics, computer engineering, computer science, or a closely related discipline. Working knowledge of machine learning and deep learning models, including
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direction, mature technologies from lab to field, and integrate sensors, data pipelines, and analytics into operational environments. This position requires deep practical experience in power engineering and
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competing structural phases and the vibrational and electronic structure in materials with defects and disorder. This effort will further seek to implement strategies to leverage machine learning techniques
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you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment. Special Requirements: Postdocs: Applicants cannot have received their Ph.D
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such as federated learning. Provenance and Reproducibility Frameworks: Build systems that enable detailed provenance tracking, schema validation, and auditable workflows to ensure trustworthy and
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Background in REST API development and database management (PostgreSQL, MySQL) Proficiency with HPC environments and job schedulers (e.g., SLURM) Experience with deep learning frameworks and neural network
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relevance to clean energy, climate resilience, and infrastructure planning. Postdocs benefit from access to world-leading high-performance computing facilities and a deeply interdisciplinary research
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machine learning, deep learning, foundation models, agentic AI systems, graph neural networks, and knowledge‑graph–based reasoning. Familiarity with integrating AI into scientific workflows at scale
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you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior