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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater
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Job Description Are you experienced in WGS data quality control and analysis from bacterial isolates? Do you have a strong interest in genomics and antimicrobial resistance (AMR)? The Research Group
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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: A Ph.D. in biophysics An Msc in physics. Extensive research experience in soft matter and biophysics. Analytical and programming skills for data analysis and ai-based analysis. Good interpersonal and
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Postdoc in experimental studies of phase behavior of ABC-miktoarm star block copolymers in thin f...
the development of GISAXS data analysis methods and is responsible for analyzing the data obtained. The postdoc will also prepare and characterize the thin films studied using e.g. spin coating and AFM. The optimal
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campus. Working at the intersection of plasma engineering, catalysis, and renewable energy systems, the postdoc will lead experimental design, reactor operation, and data analysis for the integrated plasma
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integration) Energy system optimization and planning under technical and operational constraints Scenario analysis and simulation of future energy systems, including CO2 reduction strategies Data analysis and
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studies and both qualitative (e.g. interviews or ethnographic methods) and quantitative data analysis. This postdoc is an opportunity to collaborate in an international Nordic project with external partners
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22 Apr 2026 Job Information Organisation/Company Aalborg Universitet Department The Technical Faculty of IT and Design, Department of Computer Science, Section for Distributed, Embedded and
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contribute to high-impact research on machine learning related to the analysis of IoT data in the form of multivariate time series. The DESS group is part of Department of Computer Science that belongs