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. The candidate will work closely with the CIs, post doc and PhD (machine learning) candidate, to develop choreographic structures used to generate movement and interaction capabilities that will define human-robot
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These supplementary scholarships (top-ups) are funded by the Australian Research Council to support 2 full-time PhD students who wish to undertake research in the field of battery recycling: Project
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About Data61 PhD Scholarships At CSIRO's Data61, we aim to develop top-quality doctoral students who will prosper in careers in Australia’s data-skilled workforce. Our PhD scholarship program
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electromagnetic signatures, primarily focussed on linking the data from these exciting experiments with our theoretical understanding of gravity and the most extreme regions of the Universe. I am a member of the
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interactions. Machine learning: reinforcement learning, or multi-agent systems. Signal processing: spectrum sensing, localization, or radio environment modelling. Multi-agent systems: distributed intelligence
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Status: Closed Applications open: 18/05/2025 Applications close: 9/06/2025 View printable version [.pdf] About this scholarship Description/Applicant information A PhD scholarship in
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the following knowledge and/or experience are highly preferred: Computer Vision, Signal Processing, Machine Learning knowledge and/or; Experience Industry knowledge and/or; A track record of published
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) and computer simulation (FEA) Experience in material characterisation and experimental testings Knowledge in impact dynamics Passionate and have interest in pursuing PhD degree. Experience in research
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors for tomagraphic imaging in tissue Neural network correction of distortions in acoustic transducers web...
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representation learning paradigm. This approach allows each user to disclose an incomplete yet distinct set of personal information while still learning expressive user representations from that data