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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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various skin condition/s. Relevant resources: DOI: https://doi.org/10.1007/978-3-031-43987-2_20 DOI: https://doi.org/10.1007/978-3-031-43907-0_54
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will have the opportunity to interact with gravitational-wave researchers throughout Australia and around the world. Students in my group use data from the Laser Interferometer Gravitational-wave
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We have a range of potential research projects on offer in partnership with VIFM - https://www.vifm.org/ - looking at ML techniques in predicting forensic diagnoses / image analysis, across
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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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design, contributing to our understanding of experiencing the human body as play. More information at http://exertiongameslab.org
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sensing equipment and theoretical work around play. For more information see http://exertiongameslab.org
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information see http://exertiongameslab.org
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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of techniques it offers, making ML seem an excellent tool for any task that involves building a model from data. Nevertheless, ML makes an implicit overarching assumption that severely limits its applicability