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This position is part of an exciting initiative to decarbonise and automate port operations, specifically focusing on the electrification, automation, and predictive maintenance of tugboats. The successful
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theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics and PhD students, and communicate your research at national
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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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contexts, for example to help understand and predict the performance of polysaccharides in food systems and drivers of polysaccharide evolution (manuscript attached). Once promising candidate polymers have
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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aircraft are closely monitored to see if a soot-CCM effect can be predicted. What you would be doing: You will acquire in-situ and remote-sensing data of cirrus forming downwind of flights over the past
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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effect can be predicted. You will acquire in-situ and remote-sensing data of cirrus forming downwind of flights over the past decade, along with measurements/estimates of local conditions and emissions
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microbial communities. In this role, you will develop hybrid species distribution models that combine climate and landscape data to predict how microbial taxa niches shift under changing land use and
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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum