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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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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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Your Profile: An excellent master’s degree with strong background in nano science, materials science, chemistry, chemical engineering, physics or related Experience with modeling of reaction kinetics
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support the development of a European energy system model by benchmarking future technologies and optimizing their representation within the FINE optimization modelling framework ( https://github.com/FZJ
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histological validation within murine cancer models. We offer an association with the Collaborative Research Centre 1450 “Insight – Multiscale imaging of organ-specific inflammation” graduate school
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 29 days ago
for highly-motivated and qualified candidates to work with an international team on developing cutting-edge novel demographic, statistical and computational methods in estimating, modelling and forecasting
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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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sites will not be located directly at the source of emissions, which will require transport infrastructure. As part of this work, you will model spatially resolved transformation paths for the energy