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on computer vision. The role involves developing and advancing novel algorithms for emerging challenges in computer vision, including continual learning and few-shot learning. The candidate is also expected
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
); Relevant research experience in at least one of the following: Social Robotics, human-robot interaction, human-computer interaction, Machine Learning, Affective Computing, Social Signal Processing, Computer
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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 days ago
within six months of the appointment commencement) and experience in statistics, data science, machine learning, bioinformatics, quantitative biology, or closely related fields. Candidates must possess
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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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machine-learning based methods for analyzing IoT time-series data. Your Competencies: We seek a highly motivated researcher with a PhD in computer science, proficiency in software development, and a strong
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and enthusiastic individual who meets the following criteria: Recently earned a Ph.D. in bioinformatics, computational biology, computer science, electrical and computer engineering, or a related
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role include: Essential Master’s degree (minimum) in Computer Science, Data Science, Computer Vision, Visual Media Engineering, or related subject focused on AI and Machine Learning Proficient in AI/deep
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computing platforms, containerization/orchestration tools for ML workflow management and scalability. Specialized knowledge in at least one domain: NLP, computer vision, reinforcement learning, or scientific