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Field
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approaches including stable isotope techniques, omics-based methodologies and quantitative modelling to understand nitrogen cycling within soil-plant systems under rehabilitation and development. Publish
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interpretation of data Simple process simulation and modelling General lab organization and coordination Assist supervising PhD, Master and undergraduate project students Data collation, presentation and reporting
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with experience. • Demonstrated research experience in Modelling and Simulation (M&S) and decision support analytics, stakeholder engagement and an active research agenda evidenced by research, supported
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) Research Fellowship for Master degree holder, within the scope of the R&D project “Radio4Breast - Mapping the potential of human breast-on-a-chip models for personalized radiation therapy, reference 16134
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industry partner, CSL Behring. These datasets hold critical patterns, anomalies, and insights that traditional methods miss. You will develop, test, and evaluate robust, interpretable machine learning models
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seminars etc Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental/School research-related activities and research
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, and workforce development, in addition to advanced instrumentation, including recirculating flumes, velocity measurement systems, spectroscopic tools, and numerical modeling platforms, to test new
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synthesis of timed and probabilistic behavioral models for model checking, performance evaluation, and optimization. The overall objective is to establish formal foundations that bridge static engineering
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or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the
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. Desirable (but not essential): Prior experience or demonstrated interest in economic input–output analysis, including multi-regional input–output (MRIO) frameworks or supply-chain modelling. Prior experience