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Field
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Primary Duties & Responsibilities: Assists research studies with implementation of: existing algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic
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analysis; (b) machine learning-programming and algorithmic code development; (c) literature review and data search; (d) computational modeling and numerical simulations; (e) literature review and data search
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computing and artificial intelligence. Areas of interest include, but are not limited to, the following: Quantum Machine Learning and AI: Develop novel quantum algorithms and computational frameworks
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Posting Details Position Information Job Category Faculty Position Title Assistant Professor of Computer Science Working Title Assistant Professor of Computer Science Scheduled Hours Per Week Salary
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professional image over the phone and when representing their program. Location: Hock Plaza Endocrine Oncology Durham, NC Hours: Monday-Friday 8:30am-5pm Work Performed Scheduling: First point of contact
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to join our group of neuroscientists seeking to solve grand challenges in neuroscience, healthcare, and computing. The successful applicants will contribute to this effort by creating new AI/ML models
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scientific and technical literature; designing, adapting, and implementing computational methods, algorithms, software, simulations, prototypes, and experimental procedures to meet specific research objectives
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 20 hours ago
scientific and technical literature; designing, adapting, and implementing computational methods, algorithms, software, simulations, prototypes, and experimental procedures to meet specific research objectives
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Job Description: The Electronic Production and Design Department at Berklee College of Music seeks a part-time faculty member to teach courses in SuperCollider, computational sound, and algorithmic
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public service. Job Description The Research Assistant will support the development and implementation of machine learning algorithms for cyber-physical attack detection in electric power systems