3,366 phd-studenship-in-computer-vision-and-machine-learning Fellowship research jobs
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience of C/C++/C#, TensorFlow would be beneficial
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and programming skills and experience of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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to the project. Job Requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. Min 3 years of relevant experience in computer vision, artificial intelligence, etc
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scientific conferences and publish models and scientific insights in high-impact journals Who You Are: Ph.D. in Computational Biology, Bioinformatics, Computer Science or Machine Learning related field
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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in USN’s PhD-program in Ecology within three months of accession in the position. The vacant position is part of a collaboration between the Colour Vision and Retinal Imaging Laboratory headed by Prof
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these challenges by advancing machine learning (ML)-based and data-intensive methods for the scalable design, processing, and deployment of DPPs. The advertised PhD position focuses on developing ML-based and
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image