328 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in Australia
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development. Familiarity with software development lifecycle including design and documentation of software architecture, testing/debugging skills, and version control. Experience applying machine learning
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opportunity to join the Snow Vision Accelerator Program either as a Postdoctoral Research Associate or Postdoctoral Research Fellow, based at Bell Group, Snow Vision Accelerator, Charles Perkins Centre, Faculty
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discipline, with an excellent academic record. Demonstrated expertise in computer vision and machine learning, including object detection, segmentation, and multi-object tracking in challenging conditions such
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Computer Science, Applied Mathematics/Statistics, Robotics, Physics, or related discipline, with an excellent academic record. Demonstrated expertise in computer vision and machine learning, including object
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, or related discipline, with an excellent academic record. Demonstrated expertise and leadership in computer vision and machine learning research, including object detection, multi-object tracking, and
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combatting wildlife trafficking and environmental harm. ESSENTIAL MINIMUM CRITERIA PhD in a relevant discipline such as computer science, data science, digital forensics, cyber security or a related field with
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teaching equip the next generation of leaders and innovators with the skill, commitment, and vision to protect vulnerable communities and truly advance health outcomes. It is at the heart of everything we do
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/Statistics, Robotics, Physics, or related discipline, with an excellent academic record. Demonstrated expertise in computer vision and machine learning, including object detection, segmentation, and multi
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Australia Application Deadline 9 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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an advanced AI-augmented digital platform (AiCT-Med) powered by cutting edge machine learning models trained on multiple large, aged care datasets from providers across Australia. The platform is designed