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. The department’s PhD programme is small and offers a stimulating research environment with advanced economics training in the first year. During the four years of the programme, students engage in independent
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of 2,000 and individual Training Budget of 1,000 for specialist training Project Aims and
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About the CDT The UKRI AI Centre of Doctoral Training in Designing Responsible Natural Language Processing is dedicated to addressing the increasing necessity for an ethically grounded approach in AI technologies. As NLP becomes more integral to applications like personal assistants (Siri,...
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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: This project fuses machine learning (ML) based inverse design approaches and topology optimisation (TO) to realise multiscale
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced around 7,500 academic staff members so far. They see themselves as
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developed during this work will deepen our understanding of nonlinear wave phenomena in fluid mechanics and contribute to the broader theory of interfacial flows. This PhD project is in a competition for a
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Software Development: Building the Next-Generation Trust Maturity Model Integrating DevOps practices into ML-driven systems: A Framework and Maturity Model for Continuous Machine Learning Development