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, experimental testing, or hardware-in-the-loop implementation would be an advantage. You are not expected to have expertise in all of these areas. Qualification requirements PhD stipends are allocated
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applicants will be informed whether they have been shortlisted for assessment or not. The hiring process at Aalborg University may include a risk assessment as a tool to identify potential risks associated
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-constrained communication, satellite and non-terrestrial networking, the interplay between AI/machine learning and communications, communication and sensing, quantum communications, and experimental work
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-makers and innovative teaching materials, resources and pathways for formal and non-formal language learning in a digital Europe. In this context, IRP13 investigates the impact of migration-language
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questions, and be able to think critically and develop your own scientific ideas. Previous experience with machine learning, wearable sensors, haptics and control systems is an advantage but is not required
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educational technology spaces can foster embodied and epistemic engagement with scientific practices – helping students not only understand complex concepts and procedures in the short term, but also develop
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and a wide range of consumer products. Their success depends not only on nutritional quality but also on how rapidly and completely they reconstitute in water. There is still a lack of understanding on
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PhD position in environmental toxicology and endocrine disruption: Focus on new endpoints in zebr...
chemicals (EDCs) and evaluation of a range of potential biomarkers and endpoints not yet validated in fish. Eleutheroembryo life stages (currently regarded as non-protected New Approach Methodologies (NAMs
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for the assessment. However, this is not a requirement. Framework for the PhD Programme The PhD programme is conducted in accordance with the current Danish Ministerial Order on the PhD Programme and the rules and
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed