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Field
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. Applying machine learning to New Zealand’s landslide inventories to model landslide location, character and dynamics. Integrating time-series and inventory data to develop new models to predict location
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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laude level. You are interested in both Machine Learning and Symbolic/Logic-based AI methods. You strive for excellence and have a scientific mindset. You are a loyal team player, who can work
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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integration of model checking and synthesis with machine learning will provide the key to innovative, highly scalable methods for learning, analysis, synthesis and optimization of cyber-physical systems. Based
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale