32 machine-learning-"https:"-"https:"-"https:" positions at UNIVERSITY OF WESTERN AUSTRALIA
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candidate will lead curriculum, academic governance, and faculty capability development, embedding a culture of innovation, integrity, and student-centred learning. You will champion teaching excellence
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. Demonstrated computer competency, including the capacity to maintain electronic patient records and clinical correspondence. Demonstrated commitment to and awareness of cross-cultural issues. Note: A current “C
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, Applied Mathematics/Statistics, Robotics, Physics or related discipline, with an excellent academic record. Strong foundations in applied mathematics, computer vision and machine learning, particularly
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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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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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of participation in research leadership. Broad experience with data management processes and techniques, building and running data analysis tools and pipelines, machine learning and AI techniques and tools. Position
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approach and a willingness to learn. While the appointee may not currently possess all the required skills and experience, they will be supported to develop the necessary capabilities About you PhD in plant
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. Position description: PD [Postdoctoral Research Associate (PSIC)] [522165].pdf To learn more about this opportunity, please contact Ryan Lister at ryan.lister@uwa.edu.au How to apply Please apply online
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students participating in rural research projects Collaborate with the UWA service-learning unit coordinator and RCSWA students participating in service learning projects. Identify educational and upskill
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biology, agricultural systems, climate science, or a related discipline. Evidence of development of innovative and effective approaches to teaching and learning in areas related to environmental monitoring