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2026. Please make sure you detail within the application form the programme you wish to be considered for – see School information below. You must include the title of the studentship in your application
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person or via zoom. The interview date is to be confirmed. At interview, candidates will be expected to give a short presentation on their proposed project including relevant literature, potential data
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microclimates that demand dense sensor networks and reliable data retrieval. This project focuses on developing nature-inspired hardware to deploy Internet of Things (IoT) sensors in forest ecosystems. Combining
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, the project will encompass the entire value chain using published data and expert solicitations to develop frameworks to evaluate the essentiality, safety and sustainability of contrasting product formulations
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)' as the programme of study You will then need to provide the following information in the ‘Further Details’ section: · a ‘Personal Statement’ (this is a mandatory field) - upload a document or
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synthesis or colloidal assembly is an advantage) Evidence of scientific writing and publications is a plus. Excellent communication and data presentation skills Fluent in English (German is an advantage
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DTP students in Health & Wellbeing will be members of the GUDTP Health & Wellbeing Pathway, led by Brunel and delivered collaboratively by the three universities. You can find further information about
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. These “artificially aged” oocytes reveal key ageing features. We will use this as an additional model to monitor the temporospatial changes during oocyte ageing which will allow us to generate data for a standard curve
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loads in power system dynamics and stability as system strength continues to decline. Building on existing frameworks such as the WECC Composite Load Model (CLM), you will develop and validate data-driven
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, environmental science, urban sustainability, geospatial analysis, or quantitative modelling. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods