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Environment - Wiley Online Library Additive Manufacturing: A Comprehensive Review Big data, machine learning, and digital twin assisted additive manufacturing: A review - ScienceDirect Full article: Achieving
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the direction of the project, tailoring the modelling approaches, physical focus areas, or computational strategies to your interests—whether that involves large-scale reservoir simulation, pore-scale physics
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atmospheric CO2 . Low iron input is needed for a large drawdown, offering a feasible route to help reduce climate change. Algae and resulting detritus sink to the ocean floor, locking carbon up permanently
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overuse injuries. Wearable sensors to quantify of the impact and benefit of sleep on the recovery, performance and overall wellbeing of athletes. Using big data and machine learning methods to identify
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understood. However, the increasing availability of high-resolution satellite imagery now enables the creation of detailed large-scale, multi-temporal inventories, offering new opportunities to investigate
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(e.g. neural ODEs and SDEs), identifiability and interpretability, large language and sequence models, and multimodal data integration. This position will be based at the world-leading CRUK Cambridge
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writing and implementing code alongside extracting information, trends, and patterns from large datasets. Topics to explore during this PhD project include: Investigating available software options Methods
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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and loss of migration across western Europe. Benefitting from the strong expertise of the supervisory team in stork ecology, movement analysis and spatial models, the project will leverage large
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intensity of these changes. This PhD project will ultimately enable aircraft to reroute safely and efficiently in real time as weather evolves. By merging scientific machine learning, large-scale data