142 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at Harvard University
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to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and
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Details Title Postdoctoral Fellowship in Power and AI Systems School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science/ Electrical Engineering
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welcome applications from recent PhD graduates who are interested in these or related fields, particularly those who may bring a new perspective or new technological expertise to bear on the work in the lab
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proteins important in human health and disease, including G protein-coupled receptors and other transmembrane proteins. We welcome applications from recent PhD graduates who are interested in
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, including research areas covered by the Euratom Research and Training Programme. Researchers interested in PFs should have a PhD degree at the time of the deadline for applications. Applicants who have
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Qualifications The Fellowship is meant for recent postdoctoral scholars who have completed their PhD studies in the last five years. For this year’s application cycle, applicants must have obtained their PhD
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of Engineering and Applied Sciences. The fellow will design and run human experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be
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and professional development funding will be available. The aim of this program is to expand the pool of talented academic leaders equipped to conduct research that advances health equity using
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of this program is to expand the pool of talented academic leaders equipped to conduct research that advances health equity using the frames of social medicine. Fellows will be mentored to pursue the next phase of
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric