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to develop trusted data-sharing solutions for the heat transition. Within the project, you will have the opportunity to collaborate with other PhD candidates and a postdoctoral researcher working on
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and a postdoctoral researcher working on complementary topics including decision-making under data-sharing constraints, privacy-preserving data sharing, governance and citizen participation. You will
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Python and modern machine‑learning development, including version control (Git), testing, and reproducibility; experience with cloud-based solutions (e.g., Azure) is a plus. In our international working
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skills. experience in data analysis, quantitative modeling and programming (e.g., R, python); knowledge of nutrient and/or agrochemical cycles in agriculture; excellent scientific writing skills in English
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candidates, 7 postdoctoral researchers and 1 PDEng, allowing close collaboration across disciplines including plant physiology, crop modelling, greenhouse technology and systems engineering. As a PhD candidate
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the Netherlands and the United States and will employ 11 PhD candidates, 1 EngD candidate and 4 postdoctoral researchers. You will closely collaborate with another PhD candidate developing optimization and
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the project, you will have the opportunity to collaborate with other PhD candidates and a postdoctoral researcher working on complementary topics including decision-making under data-sharing constraints
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have the opportunity to collaborate with other PhD candidates and a postdoctoral researcher working on complementary topics including energy data spaces, privacy-preserving data-sharing, governance, and
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energy/water balance, evapotranspiration, thermal comfort, heat-health interactions. Solid programming and data analysis skills (e.g. Python, R, GIS). Willingness to travel for validation-related fieldwork
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modelling is an advantage. Strong quantitative skills and interest in statistical modelling, or simulation approaches. Experience with R, STATA, Python or similar software. Ability to work independently and