148 computer-science-intern "https:" "https:" "https:" "https:" "Trinity College, Dublin" positions at Ulster University
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for metabolic disorders such as diabetes and obesity. However, their efficacy and safety in humans remains unexplored. This PhD programme will seek to take the first critical step towards clinical translation
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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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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