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programming, large-scale optimization, and simulation-based optimization; and/or Expertise 2: Experience with general methods in artificial intelligence and machine learning, including reinforcement learning
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science. Familiarity with ship data processing, ship performance analysis, machine learning algorithms (Deep learning, Reinforcement learning, etc.). Proficiency in written and spoken English - essential for data
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Learning Sciences, Computer Science, Human-Computer Interaction, Informatics, Educational Measurement and Statistics, Educational Psychology, or a related field. Required Qualifications: Relevant experience
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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processing, machine learning, and optimization theory. Strong verbal and written skills in English. Excellent analytical and problem-solving skills, and capacity to pursue independent research as
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demonstrated experience in computer vision or analysis of pathology images. The appointees will participate in a multidisciplinary collaborative research project related to development of deep learning model
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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worldwide and BNL is responsible for data compilation in North America. In order to make full use of the data in EXFOR, experience in data science, machine learning (ML) and applications of artificial
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. Qualifications Applicants must hold a PhD or equivalent qualifications in a relevant field, such as Child-Computer Interaction, Human-Computer Interaction, Learning Sciences, Educational Technology, Computer
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience