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Field
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Pose EstimationStrong background in computer vision and machine learning applied to pose estimation and visual servoing; Experience with OpenCV, PCL (Point Cloud Library), PyTorch/TensorFlow, and 3D
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. graduates and doctoral candidates nearing graduation who have research interests in applied statistics, machine learning, or computational biology to apply for our postdoctoral fellows program. This program
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation
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fields. Relevant Training or Courses (optional): Experience with R or similar programming tools, or machine learning algorithms. Geographic Information Systems (GIS). Basic and advanced statistics, time
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at https://puwebp.princeton.edu/AcadHire/position/40281 and submit a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more
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, Effects, and Criticality Analysis (FMECA), functional FMECA, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault
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references is required. To learn more about AI at Princeton, please visit https://ai.princeton.edu. Princeton University is committed to fostering a diverse and inclusive academic community. To maximize
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solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical, IoT systems. Key
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, friendly and inspiring, and the position represents a unique opportunity for career development for a hard-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling
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worked in MRI research previously or have strong computational / AI / machine learning skills used in other areas of research. Essential criteria PhD qualified in relevant subject area Ability to work as