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environments) remains an open frontier. The new MIT Multi-agent AI Postdoctoral Fellowship Program at Schwarzman College of Computing (MIT MAPS) brings together cutting-edge methods in machine learning
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, Geography, Civil Engineering, Computer Science, Mathematics, Physics or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning
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medical, dental and vision coverage effective on your very first day 2:1 Match on retirement savings Responsibilities* Researching and developing novel machine learning architectures for integration across
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assimilation, and machine learning frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 6 hours ago
be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory Science – Leveraging drug development and regulatory
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(with the potential to be extended) with the Alabama Center for the Advancement of Artificial Intelligence. Leads the forefront of research in artificial intelligence, machine learning, and data science
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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staff in exhibit preparation, including object research, selection, mounting, and installation as needed. Fellowship Learning Goals Learn and follow recognized standards and policies for collection
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who shares our values and who will support the mission of the university through their work. Qualifications: Ph.D. in a relevant field such as Learning Sciences, Human-Computer Interaction, Education
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statistical software. Learning Objectives: Learn about the implementation of the application of machine learning methodologies in plant phenotyping and genotyping for the sugarcane molecular biology lab. Learn