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manuscript preparation. The Postdoc will work under the direct mentorship of principal investigator (PI) Wen-Xing Ding, PhD, Professor in the Department of Pharmacology, Toxicology, and Therapeutics (PTT
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to the preparation for a career in research or academia, participate in the University’s Individual Development Plan policy for postdoctoral scholars, have a PhD or equivalent terminal degree (such as MD, DVM, PsyD
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fluid mechanics, computational geometry, meshing, computational graphics, computational vision, or scientific machine learning in general. Successful candidates will join a community of researchers in
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, visiting trainees, and research assistants, including technical training and day-to-day guidance within research projects. Minimum Qualifications PhD (or equivalent) in neuroscience, immunology, stem cell
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Science, or related field Knowledge and experience in computer vision, machine learning, and deep learning Good written and oral communication skills Experience in leading research projects Proficiency in basics
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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to collaborate with other team members, such as the Research Fellow who focuses on building machine learning models for perceptual quality prediction. About you The post-holder is expected to have a PhD degree (or
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Qualifications PhD in computer science, clinical informatics, statistics, or a closely related field, conferred within the past five years. Demonstrated experience in health data visualization, interactive
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robots, robotic manipulators, or locomotion systems. Knowledge of machine learning, reinforcement learning, imitation learning, or computer vision techniques for robotics applications. Strong analytical