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Mississippi. Apply 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
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Description The Laboratory for Computer-Human Intelligence (CHI-lab, https://www.x-labs.xyz ) in
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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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, excluding time associated with family planning, military service, illness or other life-changing events. Candidates must have completed all PhD requirements by the commencement of employment. Brookhaven
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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA
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/Data Science, Statistics or a related field. Experience with statistical analysis, including but not limited to regression analysis and machine learning. Self-motivated, with an ability to drive projects
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 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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members with careers that leverage the skills and unique experience they gained while serving our country, learn more at BNL | Opportunities for Veterans at Brookhaven National Laboratory . Equal
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transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short