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Field
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from sensors or other continuous data sources. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages
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an environment that is diverse, inclusive and respectful. Learn more about our lab here: https://bioniclab.seas.harvard.edu/ We are recruiting fellows from diverse backgrounds interested in solving tough problems
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distribution systems, EV charging modeling, distributed energy resources, optimization, control, machine learning, hardware-in-the-loop simulation. Expertise in programming languages such as Python, C
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to serve as the basis of published manuscripts Write manuscripts describing research results. Write grants and progress reports related to research funding. Qualifications PhD or MD required Strong
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have exceptional resources to facilitate research including access to administrative, research, and computer support staff. Required Qualifications* PhD degree or equivalent in epidemiology, gerontology
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related to the preparation for a career in research or academia, participate in the University’s Individual Development Plan policy for postdoctoral scholars, have a PhD or equivalent terminal degree
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. Knowledge of statistical methods commonly used in single-cell and spatial omics, such as Seurat and Scanpy. Experience with machine learning models, such as transformer and diffusion models. Strong written
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have been received within the last three years, 1 year of experience with machine learning, natural language processing, AI tools and frameworks, data integration, and/or explainable AI. Proficiency in
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focus on space robotics research. Experience with autonomous robotics for space exploration or satellite servicing missions. Experience with machine learning techniques for robotic decision-making and
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices