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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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biology, engineering, biophysics, computer science) and experience in 3D imaging of brain tissue, image segmentation, and handling large datasets. You are comfortable with machine learning and image
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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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publications in high-impact venues. Experience with machine learning frameworks (e.g., PyTorch, JAX) and / or computational materials methods is essential. Additionally, the candidate should possess an excellent
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, the participant will learn HPC computing technologies and techniques in genomic epidemiology and machine learning to quantify drivers of IAV evolution in swine using data generated from IAV surveillance in human
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machine learning. The specific goal is to extend new and existing visualization environments to support efficient and precise annotation of histopathology images using a combination of expert human review
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with carrying out original research to develop new machine learning approaches to link different scale geochemical-mineralogical-petrophysical datasets within a 4D geological framework. An initial focus
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for drone swarms. The role will focus on multi-agent visual perception techniques. Group website: https://personal.ntu.edu.sg/wptay/ Key Responsibilities: Develop signal processing and machine learning
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similar structures from the same population. New machine learning, sensing and digital twin technologies will be developed with the aim of driving new standards for safer, greener structures in the future
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for MND which could be translated into the clinic. The idea is to use cutting edge machine learning to create clinically actionable predictions such as the time from diagnosis to requirement for a