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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
public thematically organised into four strands: Social Inequalities; Gender, Sexuality, Race & Health; Environment, Nature & Conservation; Media, Data Cultures & Algorithms. The College/Division The ANU
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, including BIM-based SLAM, traversability map analysis, and 3D LiDAR/vision sensor fusion. • Design few-shot learning algorithms for robust detection of temporary construction objects and obstacles
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welcomes all transitioning military members to apply. You will: Develop, deploy, enhance, and maintain software applications and tools to support research computing needs Implement efficient algorithms and
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visualizations and interactive tools. Engages in best practices in data analytics and data engineering - including robust validation processes, testing/deployment procedures, algorithms for data mining, version
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computational biologist to collaborate with a multidisciplinary team to develop innovative computational algorithms and approaches to address crucial questions in cancer biology. The role will involve
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reduced teaching responsibilities. We are a federal competence center for AI education and research. Our research pushes the boundaries in numerous aspects of intelligence, including algorithms and models
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research experience (which may include experience gained as an undergraduate); demonstrated interest in computer science, progress, computational performance, algorithmic improvement; proficiency with: MS
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. The successful candidate will assist in developing novel algorithms and integrating them into robotic platforms, helping to push the boundaries of embodied intelligence in both research and practical deployment
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signature recognition and classification algorithms. Experience with Computational Imaging and Image Reconstruction techniques. Special Requirements: Visa sponsorship is not available for this position. Q
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modeling of novel radiopharmaceuticals, development of advanced image reconstruction algorithms for quantitative PET and PET/MR imaging and the development of deep learning methods to improve quantitative