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PhD Opportunity – Advanced Microwave Sensor Design for Detection Technologies The School of Electrical and Mechanical Engineering at the University of Adelaide is seeking a highly motivated PhD
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challenging problem due to the limitations of classical algorithms. These methods often struggle with the complexity and scale of accurately predicting mRNA secondary structures. This pilot research project
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Senior Engineer - Embedded Sensor Edge Computing Job No.: 684252 Location: Clayton campus Employment Type: Full-time Duration: 2 year fixed-term appointment Remuneration: $120,138 - $132,610 pa HEW
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Status: Open Applications open: 5/09/2024 Applications close: 31/12/2025 View printable version [.pdf] About this scholarship Description/Applicant information This scholarship is funded by CSIRO and Industry collaborator Kurloo Technology, with the student primarily based at Curtin University...
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environmental sensing. The incumbent will contribute to the development and deployment of real-time correction algorithms and hardware systems, leveraging GLAO technologies in collaboration with ULTIMATE-Subaru
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learning and mechanistic modelling, sensor network development, efficient data sorting and processing algorithms, real-time and model predictive control, and transformative applications in the wastewater
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Mixed-Integer Programming (MIP) solvers are very powerful tools to solve combinatorial problems that arise in many industries. Modern MIP solvers usually run a sequence of algorithms to solve
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techniques to design new and efficient algorithms that can provide strong protection during the entire life cycle of ML models used on the devices. Research Task I: Investigate ML algorithms and optimisations
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Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind
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. The project will focus on automated distributional shift detection and monitoring, invariant and distributionally robust representation learning algorithms, and deployment-time calibration with uncertainty