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areas: Generative AI Agentic AI Graph Representation Learning and Modeling Foundation Models Large Language Models Multimodal Learning Forecasting Models Basic Qualifications A Ph.D. or equivalent degree
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Models Basic Qualifications: A Ph.D. or equivalent degree in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong
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Science Statistics / Biostatistics Applied Mathematics Data Science Demonstrated expertise in modern machine learning, including at least one of the following: Deep learning (e.g., transformers, sequence models
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modern machine learning and a strong record of research accomplishment who are excited to build brain foundation models and other AI systems that advance our understanding of neural activity, brain
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deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific
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contributions. Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models. Strong programming skills in Python and experience building and maintaining research code
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on type, size, location of data centers in the US, their electricity and water demand, carbon emissions; exposure to air pollution. ● Develop and/or apply methods for causal inference and machine learning
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in internal meetings and at national/international conferences. ● Collaborate with an interdisciplinary team (bio)statisticians, data scientists, computer scientists, and climate scientists
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning