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to climate, health, and environmental challenges. Strong candidates will have experience in ML algorithms, human-centered ML, or NLP. Supported by an ARC Future Fellowship and CSIRO-NSF project. Computational
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Australian National University | Canberra, Australian Capital Territory | Australia | about 17 hours ago
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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modelling, and the economics of mental health Support economic analysis of healthcare utilisation, outcomes and costs using national datasets Develop and apply algorithms for cost-effectiveness analysis
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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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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classical-quantum algorithms for strongly correlated materials. Key responsibilities include: Research: Establish a research program, collaborate on research projects, seek and manage research funding
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people who discover them The Opportunity The Department of Electrical and Computer Systems Engineering is seeking applications for a Level A Research Fellow to contribute to a high-impact research project
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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, and may utilise iterative algorithms, machine learning and high-performance computing. Through the Monash Centre for Electron Microscopy, opportunities exist to acquire large experimental datasets using