118 parallel-and-distributed-computing-"UNIS" positions at Nottingham Trent University
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Safe and Inclusive At NTU, we continue to build an inclusive culture that encourages, supports and celebrates the diverse voices and experiences of our students and colleagues. By championing positive wellbeing, we promote an environment where all can thrive and reach their full potential. We...
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) and will collect transcriptomic, proteomic, and functional data from primary patient samples (blood, cerebrospinal fluid and haemopoietic stem cells). They will leverage advanced computational biology
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within the rotation. The PhD programme will allow the applicant to experience a wide range of established and emerging techniques that underpin research in animal production and develop their practical
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October 2025 Entry qualifications Applicants must hold a master’s degree in accounting, finance, economics, business, management, data/computer science, or a related field. We especially encourage
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. Entry qualifications Candidates must have a UK Master’s degree (or international equivalent) in Computer Science, Management Science, Business Analytics, Mathematics or a related discipline. A strong
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About Us Our award-winning Department of Communications, Alumni Relations, Marketing and Student Recruitment (CAMS) is responsible for engaging with key audiences including prospective students to support NTU’s student recruitment targets and driving forward NTU’s reputation and brand. CAMS...
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Overview Model co-creation and facilitated modelling are popular methods used in supporting group decision making in industry and public organisations. Facilitated modelling involves developing models and solutions to the problems faced by organisations, led by a facilitator and/or analyst...
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– understanding the determining factors of FDI is an important issue from a developing country perspective. Although Africa has undertaken a programme of political and economic reforms, inward investment remains
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management: strategy, planning, and operation, Prentice Hall, London, 2001. [2] M. E. Karaa, S. U. O. Fırata, A. Ghadgeb, (2020), A data mining-based framework for supply chain risk management, Computers