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technology, based on aluminium anodes and carbon cathodes. The development will be conducted in a multidisciplinary framework, implying that materials research is aided by sustainability analysis, and focused
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experiments Relevant experimental method development Surface characterisation, optical and visual characterisation Data analysis Complete the doctoral education until obtaining a doctorate Contribution to
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announcing a PhD position in Logistics focusing on Immersive media technology environments in the analysis of consumer response in circular value chains. The PhD position is organized under Department
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methods for analyses of large-scale genomic data such as genome-wide analyses, polygenic prediction, Mendelian randomization, functional bioinformatical follow up and network analysis. The project will
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technical work packages: WP1 Annoyance from wind turbines WP2 Wind turbine noise data collection and analysis WP3 Wind turbine noise source modeling WP4 Wind turbine noise propagation modeling WP5 Wind farm
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Element simulations (e.g. pushover analysis and/or incremental dynamic analysis). Moreover, the project aims to verify the ductility requirements with full-scale experimental results and explore
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and some familiarity with the preparation of tissue sections Experience working with tissue sections or organoid samples for bio-imaging applications Familiarity with image analysis tools such as MATLAB
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challenges in machine learning for short multivariate time-series analysis. By developing a multiway and multitask learning framework with built-in explainability, the project aims to deliver clinically
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this emerging field in close cooperation with our research group and the industry. The goals of the PhD project include: Analysis of the sources of CO2 with potential for delivery to the CCS facilities in
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learning is required. Experience with spectral wave modeling is an advantage. Experience with ocean modeling is an advantage. Experience with metocean data analysis is an advantage. Experience with git