39 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" "Univ" "UNIV" "UNIV" uni jobs at Aalborg University
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on development of wave energy and offshore wind. Most of our research is focused around work in our wave flume and basin. Further description of the group may be found here: https://vbn.aau.dk/en/organisations
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optimization of production systems and supply chains, including digital twins, virtual system validation, process modeling, and data-integrated decision models. Research should explicitly support managerial
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testing and condition monitoring using modern machine learning, including multimodal foundation models and related data-driven and physics-informed approaches. Research topics may include visual and real
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hardware-in-the-loop testing of integrated energy systems. The candidate is expected to have a solid understanding of system monitoring, experimental data management, and validation of thermal systems, as
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well as problem-based learning and project work (PBL). Applicants can find information about the academic content of the programmes here. Research Assistant Professors develop their own research profiles and
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areas: cyber security privacy engineering cryptography and applied cryptography computer engineering edge or cloud computing and networking. You will be part of one of the department’s research groups in
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mitigation, construction, and bio-based systems. Familiarity with integrating structured and open data practices in LCA workflows will be considered a plus. Additionally, you have a proven track record of
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on Department of Sustainability and Planning here. You may obtain further professional information from Associate Professor Ivar Lyhne, +45 5142 2310, lyhne@plan.aau.dk How to apply Your application
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for the position. Do you have any questions? If you have any questions about the position, you are more than welcome to contact us. You will find contact persons at the bottom of the jobpost. Further information
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are in part funded by a DFF: Sapere Aude project (“Building TRUST in Text: Linguistically Motivated Language Model Detection”) and an NNF: Ascending Data Science Investigator project (“LM2-SEC