119 network-coding-"Chung-Ang-University" positions at Monash University in Australia
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of the AIATSIS Code of Ethics Why Join Us Opportunity to shape lasting impact on Indigenous advancement in one of Australia's leading Universities. Work at a strategic level alongside senior leaders to influence
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to lead the design and delivery of high-performance, scalable data systems powering real-time condition monitoring across rail networks in Australia and internationally. This is a professional (non-academic
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, Indonesia, Italy, China and India. Monash fosters a thriving network of scholars, clinicians and innovators addressing some of the world’s most pressing health challenges. Within the Faculty of Medicine
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. This project concerns creating suitable models - and/or general hybrid models - as yet undeveloped. References: Comley, Joshua W. and D.L. Dowe (2003). General Bayesian Networks and Asymmetric Languages, Proc
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in the top 100 universities worldwide. Monash has six globally networked campuses and international alliances in Europe and Asia. The applicant will be based at the Clayton campus in Melbourne
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of experiments, analysis of neuropsychological and cognitive data and application of computational models. It also contributes to scientific publications and supports collaboration within a network of researchers
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record of professional excellence, you will foster collaborations and lead engagement initiatives that grow and strengthen our research collaborations with industry and alumni networks. Your contributions
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Code for the care and use of animals for scientific purposes (8th edition 2013 (updated 2021) (the Code)). The AEC is comprised of members across various categories as stipulated in the Code and includes
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actively support gender equity and diversity through initiatives like the Athena SWAN Charter and the STEMM Women Academic Network. We know that diverse teams create better outcomes, and we’re committed
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neural networks, we aim to improve the interpretability and robustness of reconstruction techniques. Another exciting direction involves self-supervised learning, which reduces reliance on fully labeled