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from protein structures and simulations Design and training of QSPR and machine learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET
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Design and implement clustering and integration approaches (e.g., network-based and subspace clustering) Use co-regulation networks for gene function and protein–protein functional relationship prediction
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manipulation in microfluidic environments Design and implement reinforcement learning algorithms for control and manipulation, first in simulation and later on real experimental setups Refine a real-time
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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic
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model surrogates using machine-learning methods to replace very time-intensive simulations. Design an efficient training strategy for these machine-learning tools, making use of existing model simulations
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: Additional Information on DAAD Study Scholarships in the Fields of the Performing Arts https://www.daad.de/en/studying-in-germany/scholarships/scholarship-database/information-performing-arts/">Additional