1,064 phd-computer-science-fully-funded-"IMPRS-ML"-"IMPRS-ML" Fellowship positions in United States
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, Geography, Civil Engineering, Computer Science, Mathematics, Physics or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data assimilation, and machine learning
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regular meetings with the research group. Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or a related field. Proven experience working with LiDAR sensors, point cloud data
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that machine learning applications are developed with ethical considerations in mind. Participate in regular meetings with the research group. Required Qualifications* Ph.D. in Electrical Engineering, Computer
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photoreceptors in marine Synechococcus using CRISPR and protein biochemistry approaches. This project is funded by the NSF Systems and Synthetic Biology Program and builds on our successful initial studies of Type
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and Applied Sciences Department/Area Electrical Engineering/Computer Engineering/Computer Science Position Description Project Deep learning plays an essential role in the operation of an autonomous
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& Surgery (9) Admissions (8) Anesthesiology (8) Biomed Informatics Biostat EPI (8) Dermatology-Derm (8) Medicine Hospitalist (8) Orthopaedic Surgery (8) Radiology (8) UP Payment Services (8) Ophthalmology (7
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Minimum Qualifications: PhD in Geology, Geography, Civil Engineering, Computer Science, Mathematics, Physics or a related quantitative field. Skills and Knowledge: Knowledge of scientific computing, data
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A full-time postdoctoral fellow position is available in Professor Wenyi Wang's lab at the Department of Bioinformatics and Computational Biology, the University of Texas MD Anderson Cancer Center
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 1 hour ago
Position Information General Information Position Number POST40 Working Title Postdoctoral Fellow, Biological Sciences Division Academic Affairs Department College of Science (Col) Work Unit
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aspects of predictive modeling. A Ph.D. in bioinformatics, genetics, statistics, computer science, or a comparably quantitative discipline (e.g., mathematics, physics), or comparable research experience