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PhD in the Engineering Hydrogen NetZero (EnerHy) CDT which is a new EPSRC-funded Centre for Doctoral Training (CDT) focusing on research and training in the areas of Hydrogen and Wind Energy. The
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Section 1 - Project Details: Studentship Start Date: 22/09/2025 Principal Supervisor Name: Julia Reiss Julia.reiss@brunel.ac.uk Other Contacts: Thomas Miller Thomas.miller@brunel.ac.uk Project Description: Freshwater ecosystems face increasing stress from pharmaceutical pollution and climate...
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role. Positioned at the grassroots level and operating from a foundation of trust, these organisations are uniquely equipped to engage specific communities and support the delivery of emergency responses
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training
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Description: This PhD project offers an exciting opportunity to co-develop and pilot a systems-based, interdisciplinary framework for circularity assessment in the water sector. Working closely with Anglian
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Project Overview The University of Sheffield, funded by the EPSRC and in collaboration with Shell, is offering a fully funded 4-year PhD studentship through the EPSRC Industrial Doctorate Landscape
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collaboration with Airbus UK and is open to Home fees status students only (full award – home fees plus stipend). Award Value Course fees are covered at the level set for Home students c. £10,070 p.a. The stipend
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research question of this PhD project is: How does extreme weather (e.g. heat waves and storms) affect the indoor environment (e.g. temperature, humidity, air quality), household behaviours and health, and
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Programme Overview This PhD is part of the Engineering Hydrogen NetZero (EnerHy) Centre for Doctoral Training (CDT), a newly established EPSRC-funded initiative focused on advancing research and
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to air pollution in the future, and planning further policy changes. This PhD project will develop statistical modelling frameworks that are able to handle large-scale, complex, and correlated time series