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Field
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outcomes Synthetic data generation (virtual patients) Statistical model checking to ensure statistical correctness of the results Machine learning–based classification and regression methods The candidate
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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international research projects covering topics such as: energy system modeling and optimization, techno-economic analyses, data science, and machine learning Execution of case studies and energy-economic
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Learning for Foundation Models’, where the aim is to adapt these models to new tasks without forgetting previous knowledge. The precise focus of the project can be defined in collaboration with
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, advanced soil analysis, and modelling. The research involves field work in Western Australia’s unique ecosystems, applying cutting-edge imaging, spectroscopy and molecular techniques, and leveraging machine
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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behavior. (2) Evaluate their effects on performance, safety, and security metrics. (3) Propose and validate mitigation and hardening techniques at the model, system, and learning levels. The targeted
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(supervised by Assoc. Prof. Ivana Konvalinka) and machine learning researchers (co-supervised by Prof. Lars Kai Hansen), you will be responsible for designing and running interactive multi-person (hyperscanning