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Reference: SATM591 Eligibility: UK, EU, and international applicants How to apply For further information please contact: Name: Professor Mark Jolly Email: m.r.jolly@cranfield.ac.uk Phone: +44-(0)1234 758466
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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At present, it is not possible to measure sweating over an extended period of time through a wearable system. Moreover, it is not yet clearly understood how sweating is linked to emotional responses
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system will manage and adjust the way information is used in real time. Cranfield University is a globally recognised postgraduate institution focused on applied research and strong links with industry
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analysis methods. You will gain expertise in integrating experimental total scattering and high-resolution imaging data with artificial intelligence and atomistic simulation tools to overcome current
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NPL, the UK’s national metrology institute, is developing a fully functioning, thoroughly characterised, local area quantum network (QLAN) linking heterogeneous nodes across its site. It will make
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climate system, yet the potential ecological consequences of climate interventions at the poles are poorly understood. This studentship will be linked to a larger project, Eco-Ice, which will provide
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to linked data. The overarching goal will be the integration of routinely collected data (e.g. molecular genomic data with clinical data from electronic health records) to address specific research questions
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. They therefore contain critical information on the nature of the volcanic system, immediately prior to hazardous explosive events. Understanding them will generate critical information on the transition between
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-error approach due to a lack of comprehensive hormone testing data. This project aims to demonstrate that frequent hormone monitoring, combined with education and symptom monitoring, will improve