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Field
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implement cutting-edge AI solutions for real-time, image-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning
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. Essential qualifications: PhD in Computer Science, Robotics, Machine Learning, or related fields. Programming skills in Python/C++ and experience with ML frameworks (e.g. PyTorch, TensorFlow, JAX). Strong
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learning and artificial intelligence Bachelor’s/ Master’s degree in computer science, mathematics, computer engineering, or relevant technical field First-author peer-reviewed published papers (or under
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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood
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protocols, develop machine-learning methods for decision-making, and validate results on lab testbeds. Collaborating with experts in cybersecurity and computer science, the researcher will publish high
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive
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). Prior experience in medical imaging & analysis and/or computer vision algorithm development using high performance computing (HPC). Publications in peer-reviewed technical/clinical journals or machine
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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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objectives. For example, digital image processing tools, such as filtering or mathematical morphology, could be evaluated to extract structural elements of road edges from images. By combining spectral and/or