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. Contact information For further information, please contact: Professor, Jens Stougaard, stougaard@mbg.au.dk Deadline Applications must be received no later than 27 November 2025. Application procedure
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heterogeneity underlying type 2 diabetes to develop strategies that better classify, prevent, and treat the disease. The group integrates wearable data, digital health tools, molecular profiling, and artificial
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essential that you: Hold a PhD in a relevant field Have experience with ethnographic fieldwork and qualitative data analysis Are comfortable communicating research clearly in writing and presentations Can
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find useful information on working in Denmark and at DTU at DTU – Moving to Denmark . Application procedure Your complete online application must be submitted no later than 22 December 2025 (23:59 Danish
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circulars, and procedures can be found on the AAU website. You can read more about the requirements for your application here . Further information Read more about our recruitment process here The appointment
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more about CatTheory at www.physics.dtu.dk . If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark . Application procedure Your
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. Contact information For further information, please contact: Professor, Jens Stougaard, stougaard@mbg.au.dk Deadline Applications must be received no later than 27 November 2025. Application procedure
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biodiversity indices from local data. Across these domains, the project combines epistemic, ethical, ontological, and historical-philosophical perspectives. It asks, for example: How do choices about data
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electroluminescence and photoluminescence imaging, preferably daylight and field-based methods. Proven skills in data analysis, image processing and machine learning. Experience with PV performance modelling, power
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the ERC Starting Grant research project “Exploiting Nanopore sequencing to discover what microbes eat (NanoEat)” with the aim to combine state-of-the-art metagenome sequencing with state-of-the-art data