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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 18 days ago
estimation in robotics, stochastic control in systems and control theory, stochastic image processing or probabilistic computer vision). The School/Department The School of Engineering brings together a
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learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image analysis, that is medical images that evolve over
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Research Fellow - Advanced Signal Processing and Machine Learning Techniques for Vital Signs Measurement from Video Images Job No.: 691722 Location: Clayton campus Employment Type: Full-time
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enterprise and are within the top 1% of universities worldwide. Apply to be part of a Horizon Europe Pathfinder science-to-technology breakthrough, targeting a paradigm shift in fabricating a novel optically
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… Requirements Research FieldPhysicsEducation LevelPhD or equivalent Skills/Qualifications Your profile PhD degree in physics or similar natural science discipline, signal/image processing, or engineering Interest
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processing, artificial intelligence, cognition and deep learning, machine learning, navigation and mapping, autonomous driving, assistive robotics, drones, dynamics and vibration, acoustics, medical imaging
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-time research experience since their PhD completion. We are looking for candidates with a PhD and a strong background in PET imaging, particularly with demonstrated expertise in the processing and
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WordPress (40%) Specific knowledge in photographic image processing (30%) Specific knowledge in text production and proficiency in English (CET) (30%) Final classification (NF) will be calculated as follows
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sciences, physics, engineering, or a related field Have experience in brain imaging (e.g functional ultrasound (fUS), fMRI; optical imaging, electrophysiology) Are proficient in the analysis of large-scale
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation