312 computer-programmer-"https:"-"Prof" "https:" "https:" "https:" "https:" "UNIV" "Univ" "UNIV" uni jobs at Monash University
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008
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-powered tools for SE/PL, including program analysis, automated repair, and software testing is sought. Appointees will bring strong technical capability to collaborate with related groups in cybersecurity
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our mission. Headquartered at Monash University, the Centre is a transdisciplinary, multi-stakeholder program aiming to mobilise survivor-centric and Indigenous approaches, interdisciplinary
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responsible for systems implementation and coordination alongside the administration of key operational elements of the academic program. This includes managing processes and data systems supporting assessment
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This is a rare opportunity to establish and grow a world-class research program at the heart of Monash’s growing quantum technologies ecosystem. We are especially keen to attract candidates whose work
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that occurs within these biological neural networks, so that these networks can be leveraged for AI applications. In addition, you will develop mathematical and computational neuroscience models
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Goal Recognition is the task of inferring the goal of an agent from their action logs. Goal Recognition assumes these logs are collected by an independent process that is not controlled by the observer. Active Goal Recognition extends Goal Recognition by also assigning the data collection task...
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communication skills and confidence working with diverse stakeholders are essential, along with advanced computer literacy. Adaptability, resilience, and the ability to thrive in a dynamic, fast-paced environment
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., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.