19 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at University of Adelaide
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/research/about-us/ AIML is the largest University based computer vision and machine learning research group in Australia, with over two hundred members including academics, engineers, research staff and
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-based health care. High level interpersonal, communication and organisational skills. Ability to contribute to a diverse and collaborative team environment. Commitment to ongoing learning. Level B: PhD in
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, epigenetics, development, and cell biology to lead and contribute to cutting-edge research. To be successful you will need: A PhD in Cell Biology, Molecular Biology, Genetics, Biochemistry, or a related area
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need: A PhD in epidemiology, public health, medical sciences or other areas relevant to primary health care Demonstrated experience contributing to the coordination of quantitative health research
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This PhD scholarship is funded by an Australian Research Council Industry Fellowship grant. It is a 3.5-year research training program. The ARC Industry Fellowship program aims to develop a strong
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at the University of Adelaide, contributing to cutting-edge research in computer vision and machine learning for space applications. This role focuses on advancing machine learning and computer vision research, with
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experience in machine learning research and development, particularly with a specialisation in such areas as: digital forensics, computer vision, biometrics (face or voice recognition, etc.) and natural
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actions working on causal AI for a changing world. The AIML at the University of Adelaide is the largest computer vision and machine learning research group in Australia with over 180 members including
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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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Criteria: A PhD in applied mathematics, engineering, computer science, data science, physics, or a related area. Strong academic track record (including peer-reviewed publications and conference