496 computational-physics-"https:"-"https:"-"https:"-"https:"-"Ulster-University" PhD positions in United Kingdom
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-motivated candidate with a 1st or high 2:1 degree in Engineering or Physical Sciences or a related science discipline. Prior experience in thin-film deposition, microscopy, spectroscopy, electronics
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. Patrick Degenaar Eligibility Criteria We are adopting a contextual admissions process. This means we will consider other key competencies and experience alongside your academic qualifications. An example
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First or strong Upper Second-class honours degree (2:1 with 65% average), or international equivalent, in Engineering, Computer Science, Physics, Mathematics, or a related discipline, and demonstrate
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to obtain) a 1st/2.1 or Master’s degree in Engineering, Physical Sciences, Life Sciences, Data Science, Mathematics, or a related field. Strong quantitative and programming skills are required. Interest in
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PhD Studentships to support doctoral research in the Arthur Scargill Archive at the University of Sheffield Faculty of Arts and Humanities 4 Year PhD Programme Funding Available UK Students View
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for feedstock characterization. 1st or a 2:1 in a Chemical Engineering, Mechanical Engineering, Materials Engineering, Chemistry, Physics If you are interested in this project, please send your CV and cover
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competitive process. (this will cover home tuition fees and UKRI stipend). The University actively supports equality, diversity and inclusion and encourages applications from all sections of society The Faculty
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, synthesise and test the compounds. Using traditional methods, the equivalent process would take months. These approaches will be applied to optimisation of drug candidates for selected cancer targets
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of first or second class UK honours degree or equivalent in a related discipline, science (chemistry/physics) or engineering. The ideal candidate should have some understanding in the area of materials
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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