23 medical-image-processing-artificial-intelligence PhD positions at University of Adelaide in Australia
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recycling: Project 1 (2 PhD students): Development and optimisation of DES battery recycling process - These projects aim to improve the DES-based battery recycling processes, focusing on investigating
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University of Adelaide. Applicants should have a background in health or medical sciences, with an understanding of variations in the healthcare system across Australia. The ideal candidate should have some
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. Application Process To apply, please email the following documents to principal supervisor Dr Feras Dayoub (feras.dayoub@adelaide.edu.au ) and Jessica Cortazzo, Manager, Projects and Strategic Partnerships
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internship project More information about University of Adelaide's Research Internships is available here . Application Process: To apply, please email the following documents to hdr_internships
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finite element methods, which demand extensive data and are costly, PINNs embed governing physical laws directly into the learning process. This allows effective management of limited and noisy data
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on mechanical and physical separation methods for efficient recovery of active materials and components from spent LFP cells as well as development of hydrothermal relithiation processes to restore
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response incentive arrangements with the national electricity market (NEM). Currently cost recovery for regulation frequency services is based on the causer pays process, which allocates the cost of sourcing
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leadership potential. Disclosure of any other awards or scholarships held by the student is required as part of the application process. Stipend The scholarships are valued at $26,250 and are paid at the rate
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an area aligned with the proposed research internship project More information about University of Adelaide's Research Internships is available here . Application Process: To apply, please email the
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advances in process-based crop models such as APSIM, their integration often remains limited. This project proposes to get more out of on-farm data streams and process models through their more formal