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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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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
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and maintenance prediction and integrate analytics into energy and battery management systems. • Collaborate with industrial partners on validation through hardware-in-the-loop testing and real-world
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of Sustainability and Planning https://vbn.aau.dk/en/persons/jja This position is specificallyaboutmodellingcascading and compound impacts of climatechange on future Danish land use in which spatial data analytics
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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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to investigate degradation mechanisms in depth using advanced analytics, signal processing, and frequency-domain diagnostics based on electrochemical impedance spectroscopy (EIS). The overarching goal
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, mechanical and durability testing, and integration with advanced machine learning models. The postdoc will collaborate closely with CEBE’s parallel work packages. Experimental and analytical data generated in
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to the project, uniting experts in battery technology and acoustic signal processing and machine learning. The goal is to harness advanced data science techniques to establish a novel paradigm for online non
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analysed. Analytical Modelling and Simulation: Development of constellations in different orbits and under multiple geometries for the evaluation of Key Performance Indicators (KPIs) related to visibility