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into the open-source CADET simulation framework, enabling fully predictive process simulations without extensive experimental calibration. Embedded in the Helmholtz Graduate School for Data Science in Life, Earth
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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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optimise the developed methods on real biological datasets; work closely with experimental partners for interpretation and validation of results Contribute to the integration of the developed methods
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experimental calibration. Embedded in the Helmholtz Graduate School for Data Science in Life, Earth and Energy (HDS-LEE), the project offers an interdisciplinary research environment at the interface
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. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and statistical multivariate methods to extract spatio-temporal spike patterns. Finally both results will be linked and
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across subsets of neurons to low-dimensional manifolds of high-dimensional space of population neuronal firing rates. Thus neuronal experimental data are to be analyzed for both aspects by PCA analysis and
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(guilt-by-association), and benchmark them against existing bulk co-expression resources Compare and optimise the developed methods on real biological datasets; work closely with experimental partners
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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