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for light-emitting devices. Epitaxial growth, including for materials such as nitrides, is highly sensitive to detailed and often narrowly controlled process parameters, including substrate and source
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and develop an optimal (efficient, cost effective, easy to use) technological operational method for detecting and estimating wallaby populations and dynamics. What you’ll do: Develop a technological
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The Level A Research Fellow will contribute to the research efforts of the Department of Electrical and Computer Systems Engineering, with a specific focus on advancing a cutting-edge research program in
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computer vision and machine learning research group in Australia -- and contribute to world-leading research projects at the CommBank Centre for Foundational AI This postdoctoral research position is part of
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) GRADE 7, £39,355 - £45,413 pa Fixed-term Ref: 095593 A Research Fellow position is available in the group of Professor M. J. Rosseinsky OBE FRS to work in a team of computer scientists and materials
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approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own
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skills in conducting data linkage analysis such as survival analysis and regression analyses. Experience in working with computer-based data acquisition and analysis systems. Ability to develop
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the Faculty, the Department of Electrical and Computer Systems Engineering (ECSE) offers internationally acclaimed programs in power systems, telecommunications, electronics, robotics and biomedical engineering
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collaboration between industry, government, and academia. The Australian Institute for Machine Learning (AIML) at the University of Adelaide is the largest computer vision and machine learning research group in
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and research protocols in compliance-focused environments. Advanced computer skills with experience using Microsoft Word, Excel and PowerPoint; specific experience in working with a range of analytical