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expertise in synthesizing, fabricating, and characterizing materials (conductive metal-organic frameworks, 2D & hierarchical structures, single-atom catalysts, etc.) for electrochemical energy conversion and
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in top-tier machine learning/AI conferences and/or leading scientific journals. Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch
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machine learning frameworks (e.g., TensorFlow, PyTorch). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications: Experience with multi-GPU model training and
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. The position will be supervised by Professor Francesca Dominici and will focus on building and evaluating a decision framework to guide the expansion of AI data centers, aligning economic opportunity with social
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, including deep learning and hands-on experience with frameworks such as PyTorch or JAX. Strong publication record in leading venues such as ICML, ICLR, NeurIPS, or comparable conferences and journals
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on track to complete all PhD requirements by the expected start date of October 15, 2026. Demonstrated expertise in modern AI/ML, including deep learning and hands-on experience with frameworks such as
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frameworks such as PyTorch or JAX. Strong publication record in leading venues such as ICML, ICLR, NeurIPS, RECOMB, ISMB, or comparable conferences and journals, and/or substantial open-source research