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interpersonal and communication skills. While not a must, a strong background in computational methods and/or statistical methods is a plus. Special Instructions Applicants should submit a formal application and
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causal identification methods. There are no teaching requirements for these open positions. Basic Qualifications: A Ph.D. or equivalent degree in computer science, statistics, economics, management science
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/deployment. Causal Inference/Experimentation: Knowledge of experimental design, randomization, and causal identification methods. There are no teaching requirements for these open positions. Basic
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computational efforts across multiple labs at Harvard’s Faculty of Arts and Sciences and Medical School. As part of this effort, the Rubin lab is implementing new methods of studying aging in vitro using brain
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agreement. Each fellow will work with 1-2 faculty mentors on research projects that cover a broad range of environmental and agricultural economics topics and methods. Faculty mentors for this program will
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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phenotypes. The lab uses a variety of experimental (functional genomic, targeted genetic) and computational (bioinformatics) tools on human and mouse tissues and using in vitro methods on human cells
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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Postdoctoral Research Fellow position in statistics, genetics, and biomedical AI. The lab develops cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected