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Intelligence or Machine Learning, with demonstrable analytical skills. Excellent research record evidenced by first-author publications in strong international journals and conferences. Proven experience in
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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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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
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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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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 and leadership in computer vision and machine learning research
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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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within a ‘Group of Eight’ University! The State Government of South Australia, CSIRO and the University of Adelaide’s Australian Institute for Machine Learning (AIML) are actively interested in advancing
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with verifiable experience in longitudinal data analyses methods, structural equation modelling, network analysis, machine learning and/or adaptive trial design. Essential are outstanding academic
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trials. In this role, you will lead the design, development, and deployment of a real-time clinical AI evaluation platform across Australia, while building your research profile in machine learning with