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The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is
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systems and swarm robotics. The project builds on well established computational and mathematical modelling techniques to achieve its aims. Departure points will be agent-based simulations, optimisation
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management, and strategic thinking skills. Outstanding interpersonal, communication, and relationship management skills. Be part of a dynamic and innovative team dedicated to making a real difference. You'll
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cutting-edge AI methodologies, focusing on combining data-driven approaches with physics-informed models to tackle challenges in MRI reconstruction. By integrating MRI acquisition physics directly into
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The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes
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. Methods To make this pre-trained model, the student will script a virtual mouse model 13 to traverse through common behavioural apparatuses within a realistic simulation tool called Unreal Engine 14
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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discover them The Opportunity Are you passionate about transforming education and inspiring the next generation of healthcare professionals? Monash University’s Department of Biochemistry and Molecular
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at the devices, and even if encryption is used to protect ML models, those models can be extracted during dynamic analysis. To secure on-device ML models, in this project, we aim to employ privacy-enhancing