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conferences and learn appropriate scientific methods and procedures. Among the key duties of this position are the following: Carry out multiphoton optogenetic manipulation and imaging of neural activity in
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 2 months ago
to the marketplace. Read more: https://www.energy.gov/technologycommercialization/office-technology-commercialization About this Opportunity The fellowship is offering candidates the opportunity to learn about the
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. Develop advanced optimization, control, or machine learning strategies for distribution systems; validate these strategies using hardware-in-the-loop or real-time grid simulators. Develop optimization
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using real-time monitoring systems. Develop AI and machine learning models to integrate real-time monitoring data Collaborate with colleagues in computer science and computer engineering to integrate
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: Artificial Intelligence / Machine Learning Knowledge Representation and NLP methods Clinical Informatics Bioinformatics Biomedical Ontology Public Health Informatics Nursing Informatics Imaging Informatics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
Requirements Applicants should have (or expect to receive before the start of the position) a PhD in Computer Science or EE/ECE on a topic in machine learning applied to natural language processing, computer
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policy-related appointment. The DOE Office of State and Community Energy Programs (SCEP) is seeking ORISE fellows to participate in a community-based opportunity. Fellows will learn about technical
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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experience in amino and fatty acid requirements and supplies. Effective use of statistics and modeling tools in support of decision-making tools. Proven ability to work independently and learn continuously
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses