361 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in Australia
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relevant to integration of machine learning and mechanistic models, and development of engines for efficient processing and visualisation of large-scale datasets and system geographic information and
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PhD in a relevant research field. Have a nationally or internationally recognised research program in eye and/or vision science research. Possess excellent leadership and organisational skills
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PRIMARY DETAIL Shape the future of autonomous drone systems through cutting-edge research and real-world innovation at Macquarie University! Salary package: $109,272 per annum (Level A.6 - PhD
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academic record. Demonstrated expertise and leadership in computer vision and machine learning research, including object detection, multi-object tracking, and segmentation. Evidence of leading research
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Although not required to apply, support and co-investment from a university or institute partner contributes to 25% of the application selection criteria. Any form of support and co-investment should be detailed in the application form. Type of assistance may include monetary or in-kind...
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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Computer Science, Applied Mathematics/Statistics, Robotics, Physics, or related discipline, with an excellent academic record. Demonstrated expertise and leadership in computer vision and machine learning research
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Full-time, 2-year fixed-term role, with the possibility of extension An exciting opportunity to join the Snow Vision Accelerator Program either as a Postdoctoral Research Associate or Postdoctoral
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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. About the Role The Senior
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning