60 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Duke University in United States
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: Qualifications include a PhD or equivalent in environmental health, epidemiology, biostatistics, or a closely related discipline. The successful candidate should be highly organized and have excellent oral and
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regarding the use of lasers, chemicals, infectious agents, animals, and human subjects, as needed. Requirements: PhD Duke is an Equal Opportunity Employer committed to providing employment opportunity without
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projects currently under grant support. Required Qualifications at this Level Education/Training PhD Duke is an Equal Opportunity Employer committed to providing employment opportunity without regard
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Training Program. This position will be funded by our NIDDK T32. Eligibility: U.S. citizenship or permanent residency required PhD applicants must have been awarded their degree or anticipated prior to June
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scientific meetings DEFINITION: The Postdoctoral Appointee holds a PhD or equivalent doctorate (e.g. ScD, MD, DVM). Candidates with non-US degrees may be required to provide proof of degree equivalency. A
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independent research activities under the guidance of a faculty mentor in preparation for a full time academic or research career DEFINITION The Postdoctoral Appointee holds a PhD or equivalent doctorate (el gl
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Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer programming, preferably python, and will ideally have experience in working with
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collaboration with Dr. Suthana and interdisciplinary team members. · Apply advanced statistical and computational approaches to investigate neural dynamics underlying memory consolidation and navigation
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automation tools for academic and translational applications. Required Qualifications at this Level Education/Training PhD Duke is an Equal Opportunity Employer committed to providing employment opportunity
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, including development of new computational tools for processing large-scale biospecimen data Creation of novel machine learning frameworks for automated scientific analysis and discovery Design and