31 data-"https:"-"https:"-"https:"-"LaTIM---Brest" Postdoctoral positions at Aalborg University
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Postdoctoral Position in Probabilistic Machine Learning for Spatio-Temporal Data Modelling A postdoctoral position is available at the Department of Computer Science, Aalborg University Copenhagen
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At the Technical Faculty of IT and Design, Department of Sustainability and Planning (PLAN), a Postdoc position in Satellite Data Processing and Machine/Deep Learning is open for appointment from
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Measurements and Data Processing as per December 15, 2025, or as soon as possible thereafter. The position is available for a period of 1 year, with the possibility of extension. In electronic engineering
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questions about the position, you are more than welcome to contact us. You will find contact persons at the bottom of the jobpost. Links: [1] https://www.cs.aau.dk/research/Data-Engineering-Science-and
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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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. 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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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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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