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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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Your Job: Develop methods and workflows to construct robust co-regulation networks from large single-cell and spatial transcriptomics datasets Integrate ontologies and metadata (e.g., tissue, cell
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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop 3D+t image reconstruction methods in a cell microscopy setting using image sequences as well as focus stacks
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rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very relevant. The steps in the development of surrogate models are building data-driven models from medical imaging
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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results and to make parameter estimation more efficient. The project will apply and evaluate these new methods at different sites and time periods, compare them with established approaches, and finally
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Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Develop methods and workflows to construct robust co-regulation networks from large
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that can stand in for slow model simulations. These tools will be used to test how model parameters influence results and to make parameter estimation more efficient. The project will apply and evaluate
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to neural population coding. As a starting point, we will build upon recent advances in graph neural networks (GNNs), particularly those described by which offer a promising architecture for modelling
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techniques more interpretable and biologically meaningful in their application to neural population coding. As a starting point, we will build upon recent advances in graph neural networks (GNNs), particularly