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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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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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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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for opportunities abroad. These grants present an excellent opportunity for recently minted scholars to deepen their expertise, to acquire new skills, to work with additional resources, and to make connections with
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teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. The University, which is based in Cambridge and Boston, Massachusetts, has an enrollment
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diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership
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(https://www.hsph.harvard.edu/lin-lab/ ), Professor of Biostatistics and Professor of Statistics. The postdoctoral fellow will develop and apply statistical, machine learning (ML), and AI methods
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, researchers, and students on foundational machine learning and biologically informed scientific applications. The position is particularly well-suited to candidates eager to apply their technical expertise in
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. ● Demonstrated expertise in causal inference, with interest in methods development. ● Experience with statistical and ML methods, including at least one of the following: Bayesian methods, deep learning
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are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning. The successful candidate will work in a highly