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a Master's degree and a PhD in architecture, civil engineering, art history or technology history, or related fields You have experience of applying for and successfully obtaining third-party funding
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Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with industry partner to tackle challenges of practical
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: Experience with analyzing GPS tracks Good data-handling skills and ability to use R (compulsary) and preferably also Python and/or GIS competently Statistical/causal inference knowledge PhD degree in a related
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