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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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measurements and interpreting complex data using advanced post-processing techniques (e.g., Distribution of Relaxation Times or DRT). The postdoc will also contribute to the development of an experimental setup
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information from Associate Professor Carsten Jahn Hansen, 9940 8286 jahn@plan.aau.dk. Youcanread more on TECH as a workplacehere Youcanread more on Department of Sustainability and Planning here Qualification
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. Proficiency with CST Studio Suite, HFSS, and related full-wave EM simulation workflows. Competence in MATLAB or Python for numerical modelling, data analysis, and optimisation. Ability to conduct experimental
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at the University. Youmayobtainfurther professional information from Associate Professor Jakob Zinck Thellufsen, +45 9356 2359, jakobzt@plan.aau.dk. Youcanread more on TECH as a workplacehere Youcanread more on
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experiments, integrating experimental data collected during loaded magnetic resonance imaging scans of the human knee joint with the ex vivo findings. By working with in vivo models, you will contribute
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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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, 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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for large-scale data collection and patient interaction. The position offers a unique opportunity to work at the intersection of biomedical engineering, app development, and health data science in a leading
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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