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Fotograf Morten Hjertø 30th April 2026 Languages English English English The Department of Ocean Operations and Civil Engineering has a vacancy for a PhD Candidate in AI-Enabled Uncertainty Analysis
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twins, human-centric systems, robotics PhD-E: Optimizing Images Quality and Deep Learning Methods for Vineyard Disease Detection. PhD grantors: University Padova (IT) & Poznan University of Technology (PL
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experience in electrochemistry, or environmental engineering and water technology. Experience with multivariate data analysis, statistics, machine learning, numerical simulations, and programming. Skills in
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computing, and to pursue new strategic research initiatives under the Department/Faculty/University. Requirements: Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information
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Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with industry partner to tackle challenges of practical
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most-lauded science and engineering programs and has also gained prominence for a breadth of educational paths, from criminology to biomedical engineering to arts and technology.The wealth of learning
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an emphasis on technology, data science and the humanities. For more details, please view https://www.ntu.edu.sg/medicine . We are looking for a Research Fellow to conduct AI for medicine research. The role
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for the efficient training and fine-tuning of machine learning models. The postdoc will closely collaborate with researchers at the Dutch Language Institute (and Radboud University Nijmegen). Selection Criteria PhD
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Sessional Lecturer - PPG2012H-S-Topics: Applied AI Systems & Governance: Technology, Policy & Practi
-world policy applications to equip students with the knowledge and tools needed to engage with AI at both strategic and operational levels. Students will learn how modern machine learning models work
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process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support process and