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multi-disciplinary team of researchers, and will be expected to participate in other projects to ensure a well-rounded post-doctoral training. Education PhD in Computer Engineering, Computer Science, or a
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Computer Science. This is a trainee position that will give promising researchers an opportunity for academic development through a PhD education leading to a doctoral degree. The hired candidate will be
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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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equivalent. Strong background in machine learning and computer vision. Prior experience in data-efficient classification, synthesis, and detection is preferable. Strong publication records in top-tier machine
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We invite applications for a Research Fellow in Computer Vision to join an exciting interdisciplinary project between the Centre for Intelligent, Sustainable Computing and the Centre for Public
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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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, or closely related subjects (such as Computer Science, Maths, Engineering or Physics), and possess a strong track record in machine learning, and/or wireless communication, radar sensing. Excellent
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the Integreat community whose members are in Tromsø and Oslo. Your role as a PhD Fellow You will be an integral part of Integreat via the UiT Machine Learning Group. You will engage in collaborative research with
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at the University of Adelaide, contributing to cutting-edge research in computer vision and machine learning for space applications. This role focuses on advancing machine learning and computer vision research, with
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems