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focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research, the successful candidate will be expected to apply for fellowship funding, contribute
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the undergraduate and graduate level. Skillsets in machine learning, applied AI and cybersecurity are especially valuable. Review of applications will begin immediately and continue until the positions are filled
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Engineering. This includes more than 9 formal courses in 8 different instructional labs. This position also supports three computer labs used to teach additional courses and ME printer services. Day-to-day
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to imagine novel task configurations and learn robust manipulation policies from just a few real demonstrations. You will work at the intersection of 3D computer vision, physical simulation, and robot learning
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: Specialist, Learning Management Systems Department: CN eLearning Campus Location: Connect Campus Employee Classification: CSV - Computer Sciences Position Type: FT - Regular Grant Funded: No Compensation
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administrative support experience. Strong computer skills including proficiency with Google Suite, Microsoft Office suite and ability to learn additional technical systems. Ability to consistently apply policies
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by the JHU equivalency formula. Preferred Qualifications Bachelor’s Degree. Five years related law enforcement or prior military experience. Classified Title: Campus Police Officer Role/Level/Range
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a wide array of family-friendly and cultural programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer
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. status in Marketing from an AACSB or regionally accredited program. The candidate’s academic preparation should qualify them to teach in one or more of the following areas: Marketing Research, Principles
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of Artificial intelligence for De-Novo molecular design Machine learning/Neuronal networks to develop novel drug discovery tools Molecular modeling and simulation Theoretical biophysical medicinal chemistry Deep