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the Section for Aquaculture, located at our campus in Hirtshals in the beautiful Northern part of Denmark. The section is a vibrant international group that holds a strong expertise in the different aspects
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chance to connect and work with leading researchers in the Copenhagen region and abroad. As part of the project, you will also have the opportunity to spend some months under the supervision of Assistant
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minimal carbon footprint, the use of locally sourced materials, and construction without binders, thereby ensuring full material reusability. Specifically, the candidate’s tasks will include: Advancing
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the supervision of Prof. Massimo De Vittorio. You will be a part of the IDUN Center of Excellence, led by Prof. Anja Boisen at DTU Department of Health Technology (DTU Health Tech). IDUN is a highly cross
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pavements that are subjected to moving, accelerating, braking, and turning wheel loads. The work will be carried out as part of the LOOPER project, supported by the Danish Energy Agency. The LOOPER project
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. These are essential components for optical quantum computers and quantum networks, where one bit of information is encoded in the quantum state of a single photon. You will be part of a team of 10-12 people between
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and protocols for assessing fish welfare and resilience and will explore strategies to mitigate welfare risks and promote positive welfare outcomes in fish farming scenarios. You will work with nearby
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, or (field) experiments), as well as familiarity with the literature on digital transformation and business models, will be assessed positively. In addition, you should be able to work efficiently as part of a
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the national center for nano- and micro-fabrication. We work in tight collaboration with a team of quantum nanophotonics theorists and a team of nanofabrication experts. We have established an effective in-house
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work at the intersection of palaeogenomics, bioinformatics, and evolutionary biology to overcome long-standing barriers in analysing degraded or low-quality DNA, enabling reliable genomic inference