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developing advanced, data-driven models and tools to integrate renewable generators securely into Renewable Energy Zones (REZs). Our goal: a reliable, affordable, and resilient decarbonised power system
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join a collaborative, purpose-driven research environment, leading innovative projects that integrate systems mapping, causal loop analysis, scenario modelling and market systems analysis to inform
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Paper (Geoscience Characterisation (data acquisition, integration, and modelling), Rock Mass Conditioning technologies, Geo-risk Management and hazard modelling, Material Flow in Deep Cave Mines, Mining
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learning and mechanistic modelling, sensor network development, efficient data sorting and processing algorithms, real-time and model predictive control, and transformative applications in the wastewater
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, optimise, and design experiments using lab-scale bioreactor systems. You will generate the high-quality biological datasets required to train the biological layer of a digital twin, a computational model
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climate resilience. Working within a world-class research environment, you’ll combine your skills in plant and crop physiology, modelling, phenotyping, data science, and statistics to uncover insights
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, or digital health infrastructure. Knowledge of the Five Safes framework or similar data access models. Ability to work independently and collaboratively in a multi-institutional project environment
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cereals, field trials, plant/crop modelling, genomics, genetics, crop ecophysiology, and agronomy. Evidence of contribution to successful external grant applications. Experience liaising and collaborating
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intracellular signalling, including design of experiments, analysing data, writing manuscripts and reports, and supporting grant applications. This position will fit into a multidisciplinary environment with
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these data with multi-omics datasets to identify regulatory or metabolic bottlenecks. It involves statistical analysis and systems modelling, and close collaboration with strain engineering and bioprocess