69 computer-programmer-"https:"-"https:"-"https:"-"Indiana-University" research jobs at Monash University
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, Gholamreza Haffari , Mohammad Norouzi In Proceedings of CoNLL, 2018. [2] Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach Trang Vu, Gholamreza Haffari In
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-cell RNA-seq datasets; (2) cross-validating the pattern in independent datasets and (3) associating observed spatial patterns with known literature in congenital heart disease. References https
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advanced immersive visualisation and visual analytics using Augmented and Virtual Reality environments in visualising and interacting with protein structures. Required knowledge Strong computer-science and
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machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) Project funding Other Funding
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, pages 919 and 964). References : D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008) [Christopher Stewart WALLACE (1933-2004) memorial special issue [and
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, pages 919 and 964). References : D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008) [Christopher Stewart WALLACE (1933-2004) memorial special issue [and
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information about the opportunity can be found here: https://careers.pageuppeople.com/513/cw/en/job/685291/phd-opportunity-indigenous-energy Monash Energy Institute and Faculty of Information Technology
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information about the opportunity can be found here: https://careers.pageuppeople.com/513/cw/en/job/685291/phd-opportunity-indigenous-energy Monash Energy Institute and Faculty of Information Technology
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position at the Faculty of Engineering, Monash University (Australia). This project focuses on developing new algorithms to equip social robots with the social, cognitive, and communicative skills needed
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physical environment with computer-generated content around the user, have reduced the barriers to practically storing and presenting a richer array of contextual information. Thanks to substantial