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to older residents. In collaboration with our industry partner, creator of companion robots that positively impact people’s lives, in this exciting project, you will investigate and develop novel algorithms
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experiments for months before the value of output y is measured for some given input x. This creates an exciting challenge for AI researchers to develop smart algorithms that can find the optimal value of input
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communication techniques and systems: Design and develop novel signal processing algorithms and communication strategies for satellite systems, acquiring essential industry capabilities and skills. Collaborate
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queries, and automating data transformations. By combining advancements in natural language understanding, algorithm synthesis, and debugging, the proposed framework will enable developers to efficiently
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systems from QC attacks, and accelerate the adoption of quantum-enhanced cybersecurity, AI, optimisation and simulation algorithms across Australian industries – aligning with the Digital Economy 2030
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for this scholarship you must: Have a first-class Honours degree in Computer Science or equivalent Have strong computational, programming, algorithms, and data analysis skills Provide evidence of adequate oral and
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computer vision and machine learning methods and adapt new algorithms to automate inspection procedures of PV plants. Given the data captured by a remotely operated drone, we first investigate the required
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nonlinear optimisation, mixed-integer programming, algorithm design and analysis, and numerical methods Excellent computing skills and experience working with relevant programming languages and platforms
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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. Wallace (1996). MML estimation of the parameters of the spherical Fisher Distribution. In S. Arikawa and A. K. Sharma (eds.) , Proc. 7th International Workshop on Algorithmic Learning Theory (ALT'96