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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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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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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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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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, 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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(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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. ● 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
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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