89 computer-science-intern-"https:"-"https:"-"https:"-"https:"-"L2CM" positions at Ulster University
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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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(SMEs) in the health and life sciences (H&LS) sector to embed and accelerate sustainable manufacturing in their businesses. Integrated research in this programme will generate evidence-based guidance
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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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academic papers in top AI and finance venues. We welcome applicants with backgrounds in computer science, artificial intelligence, or computational modeling. Skills required of the applicant: Essential
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experts, building a powerful, dual-skill profile. Completing this project will establish you as a leading expert in industrial AI and computer vision, highly sought after in the growing Smart Manufacturing
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studentship but meet admission requirements may be offered admission on a self-funded basis. Applicants who already hold an MRes or a doctoral degree or who have been registered on a programme of research
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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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Satellite and earth observation together with RE technology implementation and advanced artificial intelligence to quantify the effect of REs in the terrestrial carbon cycle. Essential criteria Applicants
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. The proposed PhD is a partnership with Age NI. Age NI have led two important online programmes recently. The Good Vibrations programme was a men’s health programme aimed specifically at men aged 50 and over. It
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intraoperative feedback on these risks. The project will combine biomedical engineering, signal processing, and clinical collaboration to design a non-invasive ultrasound monitoring system capable of quantifying