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an understanding of the project life cycle and development of project plans, objectives and documentation. Excellent written and verbal communication skills, with a high level of attention to detail and the ability
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of reconstructing the visual circumstances leading up to mate detection. The successful candidate will contribute to an Australian Research Council funded project seeking to determine how visual parameters are used
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interact with a diverse range of stakeholders (lived experience collaborators, general public, policy makers) and students. An understanding of and commitment to UNSW’s aims, objectives and values in action
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and imaging butterfly courtship interactions within experimental enclosures with the ultimate aim of reconstructing the visual circumstances leading up to mate detection. The successful candidate will
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relationships. Evidence of highly developed interpersonal and organisational skills. An understanding of and commitment to UNSW’s aims, objectives and values in action, together with relevant policies and
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of reconstructing the visual circumstances leading up to mate detection. The successful candidate will contribute to an Australian Research Council funded project seeking to determine how visual parameters are used
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Health and Wellbeing. You will support Stephanie Partridge and the Youth Well Lab research team to achieve research objectives on projects aiming to minimise the harms and harness the benefits of digital
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, including object detection, multi-object tracking, and segmentation. Evidence of leading research projects, teams, or collaborations, with measurable outcomes. Strong record of publications or equivalent
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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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detection, segmentation, and multi-object tracking in challenging conditions such as occlusions, crowded scenes, and object re-identification. Proficiency in deep learning frameworks (e.g., PyTorch