583 systems-science-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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an efficient and mature technology, yet it requires high temperatures and has a large carbon footprint. This PhD project addresses a key challenge: efficiently producing bio-methanol from abundant
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degree/ background in Business Analytics/Data Science with some experience of dealing with Natural Language Processing (NLP) and Machine Learning (ML). It is not necessary for the candidate to have prior
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. How to Apply: All applications should be made online . Under programme name, select Electronic, Systems and Electrical Engineering. Please quote the advertised reference number: FP-SA26-CP in your
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King’s College London, in partnership with the College of Optometrists, is offering a PhD studentship to examine how optometrists cooperate with other eyecare and healthcare professionals
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This 3.5 year PhD project is fully funded by the Department of Chemistry (DTP). Home students, and EU students with settled status, are eligible to apply. The successful candidate will receive
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Dynamics (CFD) and data-driven methods for aerodynamics and aeroacoustics with primary application in aerospace engineering. The PhD project is expected to start in September 2026 (but the time can be
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Session 2026/27 – Closing Date 17:00 (UK time) 11 March 2026 The online application form can be found at: ESRC White Rose DTP Collaborative Studentships 2026 The maintenance rate of £20,780 is the
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Deadline: All year round UK only This 3.5-year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will receive
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. This project will rely heavily on computational analyses, so experience on bioinformatics/informatics is required. The student will receive extensive training in big-data analysis, genome-wide/species-wide
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap