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Field
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approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
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with strong background in Computer Vision and Machine Learning. Position Overview: In this position, you will investigate the existing solutions for assessing datasets and developing a novel tool relying
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern thinking, connecting decades
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before application deadline. Strong programming skills and experience with AI, computer vision, image analysis and deep learning are advantages. Knowledge of hematology, cytology and pathology is a plus
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development of computer systems for data analysis, development of machine learning methods, and the clinical use of technology. Within the research groups you will therefore work together with computer
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with machine learning techniques for robotic decision-making and intelligent control for tasks with high uncertainties. Experience with research on multi-agent collaboration and decentralized control
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from multiple disciplines and institutions. Required: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong background in machine learning / computer
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devices, and edge computing platforms. Deploy machine learning models to enhance process control and system responsiveness. Collaborate with industry partners to identify research challenges, co-develop