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Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired, method-driven theory to application-driven research. Please find more
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sampling strategies in Python and C++ Apply your framework to analyse real biological datasets to demonstrate robustness, interpretability, and practical impact Contribute to open-source software tools
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired, method-driven theory to application-driven research. Please find more
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data sets, which have to be evaluated in order to obtain a holistic understanding of very complex systems. Visit HDS-LEE at: https://www.hds-lee.de/ The position is placed at the Institute for Advanced
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) Use co-regulation networks for gene function and protein–protein functional relationship prediction (guilt-by-association), and benchmark them against existing bulk co-expression resources Compare and
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of results through high-quality publications and open-source software contributions Your Profile: Master’s degree in chemical engineering, biotechnology, computational biophysics, bioinformatics, data science
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to apply E-mail personal@ufz.de Website https://recruitingapp-5128.de.umantis.com/Vacancies/3332/Description/2 Requirements Additional Information Website for additional job details https://recruitingapp
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mentorship in the career development of the student. The program language is English. We offer: a highly international research environment and state-of-the-art technology four years of full funding through
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geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics