79 algorithm-development-"LIST"-"Meta" positions at Cranfield University in United Kingdom
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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from such machines to derive algorithms expressing their state of health and next maintenance needs. A background in both engineering and machine learning would be useful, although help is readily
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, government, and wider society. In the REF2021 review of UK university research, 88% of Cranfield’s research was rated as ‘world-leading’ or ‘internationally excellent’. This project will develop a robust
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will handle real human waste samples to develop robust protocols for solid and liquid waste characterization, microbial profiling, and safety validation, including pathogen screening. Focus areas include
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very well the behaviour of these cryogenic hydrogen pumps, in order to master their integration into the hydrogen system. The primary objective of this research in collaboration with Airbus is to develop
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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potential health impacts. Water utilities across the UK, including Anglian Water, are developing strategies to meet new regulatory guidelines and enhance the resilience of water supply systems. Anglian Water
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Design and Manufacturing Engineering to Tackle Global Sanitation Challenges - MSc by Research or PhD
to create solutions that are not only functionally excellent but also economically viable for large-scale deployment in diverse global contexts. This project aims to develop comprehensive design and
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efficiency, and resilience. This research directly addresses these challenges by developing high-performance, secure, and adaptive AI-driven architectures. With increasing global investments in AI-powered
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems