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, Medicine, or Engineering) demonstrated experience or knowledge of one or more of the following: computational algorithm development working with medical images, in particular CT or cone-beam CT a
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 23 days ago
: PD&PEWER Level A&B Postdoctoral - Research Fellow _ CSS.pdf This position will provide opportunities to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited
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with implementing these algorithms on fully error-corrected fault-tolerant quantum computers. About You The incumbent is expected to have relevant experience in developing quantum architectures, error
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new algorithms to analyse compliance of CER according to relevant Australian standards (AS 4777.2020.2, CSIP-AUS, IEEE). Creating new algorithms to analyse curtailment of CER according to relevant
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
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postdoctoral researcher with: A PhD (or near completion) in Computer Science, Computational Biology, Mathematics, Bioinformatics, or a related discipline. Proven expertise in machine learning and algorithm
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program, you will perform grant funded research developing novel algorithms or building new dynamic imaging systems that account for patient motion (breathing, cardiac, etc.) during medical imaging and
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imaging. Within our research program, you will perform grant funded research developing novel algorithms or building new dynamic imaging systems that account for patient motion (breathing, cardiac, etc
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environments. Access proprietary market data and institutional infrastructure, including cutting-edge computing environments and live market simulation tools. Translate research into production, contributing
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data into clinical impact. They will be responsible for developing algorithms for image analysis, creating predictive models for disease progression, and identifying patterns in imaging data