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October 2026 Additional Funding Information This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an
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of study Full-time Start date 1 October 2026 Funding This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees
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-efficient and high-performance photonic devices have been driven by the quantum revolution. This PhD studentship aims to develop novel materials and components that facilitate strong light-matter interactions
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. Optional experience in co-supervising MSc theses related to your PhD, as well as assisting with undergraduate labs (with additional pay). Career paths: Alumni from PinhoLab are highly sought after by
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) release. Understanding these fundamental processes will pave the way to identify therapeutic targets. This PhD studentship offers a unique opportunity to work within an interdisciplinary collaboration
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Subject area: Drug Discovery, Sustainability, Laboratory Automation, Microfluidics, Machine Learning Overview: This highly interdisciplinary 4-year funded PhD studentship will contribute to cutting
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explaining your motivation for applying to this PhD Studentship to: Supervisor Review Form . Our supervisors will perform a comprehensive review to long-list candidates. Deadline: 8 January 2026 Stage 2
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retreat cannot be reversed even with rapid CO₂ drawdown. This PhD will identify early signals by the 2030–2050s, targeting specific Antarctic glacier systems that are vulnerable to early retreat (Needell et
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Woodburning has emerged as the single most important source of primary fine particulate matter (PM2.5) in Birmingham and likely many other UK cities. Exposure to PM2.5 from wood stoves contributes to hundreds of premature deaths each year in Birmingham and imposes a significant burden on public...
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multimodal data, ultimately uniting rigorous machine learning foundations with biological discovery. Project details This PhD project will contribute to the development of generative models for multimodal data