76 machine-learning-"https:"-"https:"-"https:"-"https:" positions at Harvard University
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and physics-informed learning algorithms for scalable energy management and control Engagement with industry stakeholders to guide practical implementation and scale-up strategies Ideal candidates will
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alongside other scientists in the Lichtman Lab and the Neurotechnology Core Facility. They will have the full intellectual support of highly experienced SEM microscopists, engineers, computer scientists, and
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their appointment in a Harvard department of their choosing, but otherwise are expected to conduct independent research. They may not take courses for credit or teach more than one course per year, and they
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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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on its tradition of excellence to train future leaders in the field. Housed within the Department of Social and Behavioral Sciences, our Center provides academic, research, and service-learning
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22, 2025. 1. Cover letter, including a description of the candidate’s teaching/advising philosophy and practices as well as their approach to creating a learning environment in which every student is
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with bioinformatic pipelines and approaches for working with methylation data, or a willingness/ability to learn these methods. The appointment is for one year with possibility of renewal based
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or other investigators interested in learning about research at the Center or partnering with the Center. Assist with course development at Harvard Chan and/or in ExecEd, HarvardX, or short courses. Aid in
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and unrealistic implications of models in areas such as game theory, choice under uncertainty, belief updating, social learning, etc. Per the list of key responsibilities and qualifications below
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Postdoctoral Fellow with Assistant Professor Tracy Ke. Assistant Professor Ke’s lab focuses on research in high-dimensional data analysis, machine learning, social network analysis, text mining, bioinformatics