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of context-aware, distributed, and embedded cyber-physical systems, with a particular focus on Internet-of-Things (IoT) and the computing continuum eras. Our vision is to pioneer advancements in high-tech
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on the modelling and optimisation of PRO systems using advanced Computational Fluid Dynamics (CFD) and Machine Learning (ML) techniques. This role offers an exciting opportunity to contribute to cutting-edge
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, and embedded cyber-physical systems, with a particular focus on Internet-of-Things (IoT) and the computing continuum eras. Our vision is to pioneer advancements in high-tech distributed and embedded
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engineering, operations research, computer science, economics, or a related field. You must have demonstrated track record with publications in recognised and leading journals based on experience in developing
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hardware accelerators, or quantum information science. Responsibilities and Qualifications Your primary responsibilities will be centered around the fabrication and characterization of TFLN/TFLT PICs
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the responsibilities and qualifications stated above and the following general criteria: Research experience Experience and quality of teaching Research vision and potential International impact and experience Societal
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the following general criteria: Experience and quality of teaching Research experience Research vision and potential International impact and experience Societal impact Innovativeness, including
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of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment