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PhD Scholarship in Development of Cement-Free Living Building Materials for Sustainable Construction
8 Sep 2026 Job Information Organisation/Company Technical University Of Denmark Department DTU Construct Research Field Engineering Researcher Profile First Stage Researcher (R1) Positions PhD
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PhD Scholarship in Development of Cement-Free Living Building Materials for Sustainable Construction
Do you want to launch your scientific career by developing the next generation of living building materials for sustainable construction? Join DTU Construct as PhD candidate and contribute
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6 Aug 2026 Job Information Organisation/Company Technical University Of Denmark Department DTU Construct Research Field Engineering » Civil engineering Engineering » Mechanical engineering
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https://www.aau.dk/uddannelser/efteruddannelse/master/it-og-laering . Organizational and Academic Leadership PhD students are expected to build and maintain professional working relationships both
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. The position is covered by the Protocol on Job Structure. You can read more about salary levels in the state sector www.loenoverblik.dk (in Danish). Responsibilities and tasks in the PhD programme Carry through
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is expected that candidate awarded the PhD fellowship will be able to commence the PhD degree programme on 1 February 2027. Where to apply Website https://phd.arts.au.dk/applicants/open-and-specific
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covered by the Protocol on Job Structure. Salary range starts at DKK 31,800 / 4,200 per month (August 2026 level). Option B: Getting into a position on the integrated MSc and PhD programme Qualifications
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dissemination is expected to focus on leading Human-Computer Interaction venues. For further information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Help to build the digital
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. For example, in the anomaly detection setting, we often assume that only healthy images are available during training. Using this, we wish to learn the appearance and structure of healthy anatomy and detect any