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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 3 months ago
diverse fields including engineering, environmental science, policy studies, and business to address complex decarbonisation challenges. Click Apply now for more information on our application process
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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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throughout the water/wastewater value chain, allowing for more informed and robust strategies toward sustainability and circularity. Skills/Experience Required: Process systems engineering, wastewater
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53 Faculty of Earth Sciences, Geography and Astronomy Startdate: 01.09.2025 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.08.2029
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are looking for: A PhD (or have submitted your thesis before taking up the role) in Materials, Physics, Chemistry or a closely allied discipline Expertise in 4D-STEM data acquisition Expertise in cryogenic
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scientific evidence, methodology and data. Strong interpersonal, communication and organisational skills. Please note, a DBS check will form a condition of the offer. Personal qualities Strong work ethic with
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the recording of substrate-borne vibrations produced by earthworms (and other invertebrates) in situ. To fully exploit this emerging technology there is a need to unravel the meaning within the data. As such
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Kamalan Jeevaratnam , Dr Bonn Lee and Dr Charlotte Edling Entry requirements Open to candidates who pay UK/home rate fees. See UKCISA for further information . Starting in January 2026. Later start dates
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Chemistry, Physics, Materials Science, or a related discipline. The successful applicant will demonstrate strong interest and self-motivation in the subject and the ability to think analytically and
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, finance, and healthcare, where data integrity and system reliability are non-negotiable. This PhD project addresses the integration of robust security measures within AI-enabled electronic systems