80 engineering-computation-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Ulster University
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reinforcement in both the site of the insertion of the tufting yarn and between the tufts, whereas z-pinning does this at the site of insertion. 3D wovens involve layering and interweaving fibres in a computer
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generator (Python toolkit), evaluation metrics suite, and academic papers in top finance and information systems venues. We welcome applicants with backgrounds in computer science, data science, or
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Summary Applicants are invited to undertake a 3 year PhD program in partnership with a global leader in blood- and cell-based therapies (Terumo Blood and Cell Technologies Ltd). This opportunity
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for at least the three years preceding the start date of the research degree programme. Applicants who already hold a doctoral degree or who have been registered on a programme of research leading
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applicants from diverse academic and professional backgrounds, including (but not limited to): Film, Television, and Media Studies Artificial Intelligence and Computer Science Creative Arts and Practice-based
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wide range of disciplines including engineering, chemistry, mathematics, and computer engineering. The diversity of this research theme means that the student will potentially cross these disciplines
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, financial technology, policy analysis, or academia. Ideal candidate: Background in computer science, data science, finance, economics, or related quantitative fields. Strong programming skills (Python/R
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Positioned within Ulster University’s School of Computing, this research theme advances cutting edge artificial intelligence and Internet of Things (IoT) solutions for healthcare and wellbeing
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manufacturing engineering and biosensing enabling the fabrication of highly sensitive, room-temperature quantum sensors using scalable, low-energy processes. Essential criteria Applicants should hold, or expect
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clinical translation of this technology and (3) expedite commercial exploitation of this technology. Specific objectives: To generate a range of tumour tissue samples from in vivo models treated with SGEN-33