462 computer-programmer-"https:"-"U"-"UCL" "https:" "https:" "https:" "https:" "https:" "P" "U.S" positions at Monash University in Australia
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Research Fellow - Environmental Informatics Hub Job No.: 680160 Location: Clayton campus Employment Type: Full-time Duration: 2 year fixed-term appointment (with the possibility of an additional 2
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This project draws on a recent Dagstuhl Seminar (https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=18322) that brought together leading experts from industry and academia, including those who
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Metallurgy and Corrosion cluster, working within a multidisciplinary team spanning theory, advanced characterisation, and computational modelling. This environment provides an excellent platform for developing
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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control
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Centre for Health Economics, Monash Business School, PhD Program 2025 Job no.: 625101 Location: Caulfield campus Duration: 4.5-year fixed-term appointment Employment type: Full-time Remuneration
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Global Scholars Program Grant The Global Scholars Program Grant is available to students enrolled in the Bachelor of Global Studies to help support the overseas study requirement of the course
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'', Springer (Link to the preface [and p vi, also here]) Wallace, C.S. and D.L. Dowe (1994b), Intrinsic classification by MML - the Snob program. Proc. 7th Australian Joint Conf. on Artificial Intelligence, UNE
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help shape the operational success of a leading clinical school. For more about STM, visit: https://www.monash.edu/medicine/translational About Monash University At Monash , work feels different. There’s
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Automated Program Repair (APR) is the grand challenge in software engineering research. Many APR methods have shown promising results in fixing bugs with minimal, or even no human intervention
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reinforcement learning. In International conference on machine learning (pp. 2107-2128). PMLR. - Péron, M., Becker, K., Bartlett, P., & Chades, I. (2017, February). Fast-tracking stationary MOMDPs for adaptive