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fabrication facilities as well as high performance computing (HPC) facilities at QUT. PhD2: Pore-network modelling of reactive transport As a PhD student, you will develop efficient pore-network modelling
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package totalling approximately $47,000 per annum tax exempt (2025 rate) a four-year Research Training Program (RTP) Fee-Offset a four-year project expense and development package of $13,000 per annum a
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Transfer Partnership (KTP) to develop advanced AI capabilities that will unlock the next level of market insights in the Kids, Parents and Family sector. Employed and supported by an academic team from
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, Italy and the UK. The aim of this research is to understand the emergence, spread and transmission of drug-resistant pathogens under the One Health concept, and to use this knowledge to develop
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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operations. The PhD will develop and implement artificial intelligence and data-driven methods for early anomaly detection, root cause diagnosis, and failure prediction on industrial systems, leveraging
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on developing advanced methods for semantic structure extraction, conceptual and argumentative flow reconstruction, rationale-aware content generation, metacognitive prompting, and adaptive personalization. Core
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• Produce research products such as well-documented algorithms and code, software, and research publications • Prepare results for publications, work with collaborators in writing publications, and, in some
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assistant, you will contribute to the work group Machine Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data
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, Bayesian statistics) - Remote sensing theory (e.g., radiative transfer physics; algorithm development) - Remote sensing measurements and instrumentation, including calibration and validation, experience