732 assistant-professor-computer-science-data-"https:"-"https:"-"https:"-"https:" positions in New Zealand
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National Diploma or higher demonstrating strong mathematical and technical writing skills · Solid computer literacy and confidence working with digital assessment systems Mō Mātou | About us Toi Ohomai
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About the scholarship Application status: Not currently open for applications Applicable study: BSc or BAdvSci(Hons) in Computer Science Opening date: 24 September 2026 Closing date: 5 November
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stakeholders as required. Skills and experience | Kā pūkeka me kā wheako You bring: • A passion for data engineering and enabling data driven decision making. • Experience with modern data platforms
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Data Integrity Coordinator-2600521 __________ DIGITAL LEARNING ENVIRONMENT PROGRAMME OFFICE OF TRANSFORMATION AND IMPROVEMENT | TE TARI WHAKAUMU ME TE WHAKAPAI AKE About Our Team | Mō Te Tīma
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across undergraduate and postgraduate programmes, particularly within the applied data science programme. Apply innovative, research-informed teaching methodologies, including supporting learners across
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2 Apr 2026 Job Information Organisation/Company UNIVERSITY OF WAIKATO Research Field Computer science Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Application Deadline
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Professor John Chen and the Chen family. Professor Chen is an alumnus of the Faculty of Engineering at Waipapa Taumata Rau, University of Auckland, and taught in the Faculty for more than 35 years, including
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for a Lecturer position in Data Science/AI. We seek an outstanding researcher and teacher who will contribute to our Data Science, Artificial Intelligence, and broader Computer Science programmes
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strong research background. You will be mentored by Professor Richard Green and located within the Computer Science and Software Engineering. The position will also involve extensive collaboration with
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You bring: • A passion for data engineering and enabling data driven decision making. • Experience with modern data platforms (e.g., Azure Databricks, Kafka) and building scalable pipelines