34 data-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" research jobs at Aalborg University
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unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within verification and model checking, embedded and cyber-physical systems, data-intensive
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. The support will consist of: organization of chapter meetings (online and not); reference management; figure drafting and data collection; checking traceability, chapter overlaps or inconsistencies; and chapter
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with Danish and international industrial partners. More information about the AI RF Sensors group is available at: https://www.es.aau.dk/research/ai-rf-sensors Qualification requirements Appointment as
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and postdoc cohort positions at http://www.capex-p2x.com . The research area will be within design and modeling of pulsed power supplies utilizing power electronics technology for plasma generation
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members. Join us in making a positive impact on society! More information about the Department's research profile can be found here: https://www.communication.aau.dk. Qualification requirements Appointment
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to data from various sensors and radio signals? This is the main underlying theme to be explored within this postdoctoral position. The appointed researcher will investigate how AI embedded in physical
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. The work will explore adaptive MAC designs that leverage waveform structure and context information to improve user management, access efficiency, and robustness. A key aspect of the research is the joint
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workplace here You can read more on Department of Sustainability and Planning here You may obtain further professional information from Associate Professor Troels Krarup, e-mail troelsmk@plan.aau.dk or phone
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The project may address national or international problems, and should do so using appropriate methods, qualitative and/or quantitative. Access to Danish data sources, such as registry data, respondents
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charging strategies for lithium-ion batteries. The goal is to integrate model-based (digital twin) and data-driven (AI) methods to design and experimentally validate optimized pulse charging protocols. A