14 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr"-"NTNU" PhD positions in Switzerland
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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of Zurich and Wageningen University & Research. The four-year STEPS project focusses on developing data-driven and machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite
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of measurement systems, signal processing and analysis and the assessment of measurement accuracy, robustness and long-term stability. The resulting data form the basis for model-based approaches to evaluating
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(completed or near completion) in a relevant field (e.g., Environmental or Soil Sciences, AI, Data Science, Geosciences). Basic knowledge of soil science and strong interest in AI and soil health. Experience
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programs . Application Form & documents to upload You will first have to fill in the online application form . Requested information: your personal details your academic background the doctoral program you
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of electron spectroscopy experiments Modelling of experimental data Your profile The position is immediately available for a candidate with a master's degree (or equivalent) preferably in physics, physical
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) influence system performance and trade-offs. The research will combine analytical modelling with data-driven and AI-based methods, for example for scenario generation or uncertainty exploration. The PhD will
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member of the Department of Ancient Civilizations at the University of Basel. For further information, please contact the coordinator of the Doctoral Program of Basel Ancient Studies Mr. Hans-Hubertus
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analyze how these factors influence vote choice, and how the role of quality in elections can be strengthened. Empirically, the project combines surveys, survey experiments, and text-as-data methods.
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have since helped halve global mortality, but this progress is threatened by rising insecticide resistance. We build quantitative, data-driven models to forecast the spread and impact of resistance