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Field
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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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investigators to acquire essential academic credentials and research skills with the purpose of increasing competitiveness for MD, PhD, or MD-PhD programs in biomedical sciences. Primary Responsibilities Devote
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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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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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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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students to drive forward a diverse research program focused on machine learning and policy evaluation. The following tasks are designed to culminate in co-authorship on research papers: Plan and perform
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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