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Contribute to high impact scientific and clinical projects in the rapidly growing research field of AI in medical imaging and cardiac mapping Located at South Eveleigh in the Faculty of Medicine and
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WeChat Stay in touch with the University and your alumni community on WeChat. Join us at UniMelb1853. How can we help? FAQs and contact details Find helpful answers to common questions or contact the alumni office. Stay in the know Learn how to set up your UniMelb account to access perks and...
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My research connects stars, dust, and gas, with the goal of building a unified multidimensional picture of the Milky Way and nearby galaxies. I am particularly interested in the lifecycle of matter
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Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large
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Queensland Brain Institute Part-time (80%), fixed-term position for up to 6 months Base salary will be in the range $83,689 - $111,431 + 17% Superannuation (Academic Level A) Based at our St Lucia
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Are you interested in applying your AI/DL knowledge to the medical domain? This project focuses on the use of AI in Medical Imaging (e.g. CT, MRI, X-Ray, Ultrasound, etc). The work includes
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I specialise in the numerical modelling of high-energy particle collisions , such as those occurring at the Large Hadron Collider. Accordingly, most projects I offer straddle the intersection between theoretical and computational high-energy physics. The research contributes to the...
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 14 hours ago
Classification: Senior Manager 3 Salary package: Competitive salary plus 17% superannuation Term: Full time, Continuing (contingent funded) This position is continuing (contingent funded
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Volunteer Lay Animal Ethics Committee Members - Monash University Monash University has a national and international reputation for exciting, ground-breaking research that improves health, solves
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one