77 parallel-computing-numerical-methods-"Simons-Foundation" positions at Newcastle University
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industrial practice relies heavily on empirical optimisation, leading to inefficiencies in energy use and impurity removal. This PhD project proposes to develop a Coupled Computational Fluid Dynamics-Discrete
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continuous operations at lab scale. In Situ Product Recovery (ISPR) evaluation: Test ISPR methods to boost productivity and energy efficiency. Bioprocess modelling: Employ simulation, techno-economic analysis
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tissue, radiologists insert a localisation device (RFID, magnetic seed) into the tumour pre-operatively. Wireless methods hold promise however, due to the scale of current RFID tags, delivery is
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vegetation monitoring, and potentially numerical modelling. The project is a close collaboration with its sponsor, the Environment Agency, meaning your findings will inform future levee design, inspection, and
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on empirical optimisation, leading to inefficiencies in energy use and impurity removal. This PhD project proposes to develop a Coupled Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) model
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/C6 sugar utilisation and tolerance. Fermentation trials: Optimise batch, fed batch, and continuous operations at lab scale. In Situ Product Recovery (ISPR) evaluation: Test ISPR methods to boost
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) into the tumour pre-operatively. Wireless methods hold promise however, due to the scale of current RFID tags, delivery is problematic for both clinicians and patients. The PhD provides an opportunity to develop
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. You’ll gain experience in spatial analysis, fieldwork, soil and vegetation monitoring, and potentially numerical modelling. The project is a close collaboration with its sponsor, the Environment Agency
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a barrier to bionic vision to date, then develop advanced methods to talk to the brain. Our aim is to utilise this technology to restore sight to those blinded by diseases such as retinitis pigmentosa
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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability