409 computer-programmer-"https:"-"FEMTO-ST"-"U" "https:" "https:" "https:" "https:" "https:" "Dr" positions at Monash University
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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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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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and data-driven insight. Your work will span engineering design and testing, laboratory and field experimentation, trial coordination, advanced data analysis, and research administration. You will plan
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computational techniques can be combined with classical systems to improve performance, scalability, and solution quality for tasks such as: Similarity search and nearest-neighbour queries Graph and routing
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working with SPF animals, barrier facilities and specialised equipment such as biosafety cabinets and autoclaves Strong organisational skills, sound computer literacy and a high level of accuracy in record
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workstreams of the Malaysia Cyber Enhancement Program (MUM CEP). This role centres on the migration of four Malaysian Active Directory environments into Monash’s centrally managed AD and the coordinated
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., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.