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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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kinetic modeling Experience with (or willingness to learn) 3D modeling, CAD software, and 3D printing What We Offer: TV-L (E13, 67%) collective agreement ( Current TV-L pay table [pdf-Datei] ) A three-year
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available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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management platform that connects institutes to facilitate a rapid and efficient exchange among experimental and computational groups Devising an approach in invertible predictive modeling that links
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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Management (SLUSE) IPB: Natural Resources and Environmental Sciences (NRES) UPM: Environmental Biotechnology/Environmental Engineering/Environmental System and Modeling UGM: Planning and Management of Coastal
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plant genetic mechanisms that coordinate mycorrhizal interactions with plant P and water status, root system development, and soil microbial communities. Using maize and rice as models, we will: 1
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model complexes. This research is part of a large, funded collaborative project supported by the Swiss National Science Foundation, involving partner researchers based in Germany, Switzerland, and France