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Field
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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About the Opportunity Conduct research on machine learning, control theory, and synthetic biology. The work will combine tools from dynamical systems, control theory, and the theory of algorithms
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, pharmacological and chemogenetic experiments in rodents to delineate mechanisms underlying drug abuse. Conduct conditioned place preference, and learning and memory tasks, intracerebral infusions, and
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
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therapeutics and antimicrobial peptides. The Postdoctoral Researcher will apply computational modeling, structural analysis, and machine learning approaches to support mechanistic studies of cellular signaling
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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across learning sciences, computer science, machine learning, HCI and education research. Research Role Research themes for the NTO Postdoctoral Associate include, but are not limited to: Developing
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months ago
substantial intellectual independence and opportunities to collaborate closely with machine learning scientists developing predictive models of cellular function. Machine learning experience is not required
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substantial intellectual independence and opportunities to collaborate closely with machine learning scientists developing predictive models of cellular function. Machine learning experience is not required
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looking for inquisitive, creative, and passionate researchers with a PhD, MD, or MD/PhD (or related field such as genetics, genomics, computational biology, biochemistry, machine learning, population