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Field
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experience with empirical research and a strong aptitude for quantitative and technical methods. Experience & competencies Experience with Python and machine learning, preferably applied to medical imaging
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, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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models for high tech industry applications. Your results are expected to be published at leading international venues in machine learning, computer vision, robotics and radar, such as NeurIPS, CVPR, ICRA
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machine learning or computer vision models Technical competencies in one or more of the following: bio-digital systems, biodesign, applied machine learning, computer vision, computational biology, and/or
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, vegetation occlusion, and low illumination conditions; - Contribute to the definition of resilient perception architectures for outdoor robotic operation. 2. Machine learning-based resilient perception
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applied to computer vision and image analysis. Analytical thinking and problem-structuring skills, including the ability to abstract complex systems, identify core research challenges, and develop
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into the transmission X-ray imaging regime. The developed techniques will be validated on real data. As a candidate, you must have a strong background in machine learning, computational imaging, and/or computer vision
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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning and neural networks Basic