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the boundaries of cellular reprogramming by introducing scalable computational methods that streamline the discovery of reprogramming targets and control strategies. A key innovation of EdgeCR is its
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. The position focuses on frequency-domain electromagnetic (FEM) and transient electromagnetic (TEM) methods. The successful candidate will contribute to the development of an inversion framework for the joint
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., omics or clinical data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, with a focus on omics data
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live in. Your role Research related to the following areas: Mathematical statistics, Machine Learning, High-dimensional statistics, Robust estimation methods, Probabilistic foundations of mathematical
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, single-cell spectroscopy, multicolour fluorescence and numerical modelling. This multidisciplinary approach will significantly advance our understanding about the resilience of coralline algae to projected
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Do you have experience with modelling structures subjected to dynamic loading? Are you interested in data-driven methods for modelling applied loading? Are you eager to share your knowledge within
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, or similar disciplines Graduate students expecting to receive their PhD within six months can also apply Experience in the advanced analysis of genetic or proteomic data Interest in learning methods
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, communicating and solving any potential problems that arise with analyses or other aspects of research projects; Contributing to reports, presentations and publications by preparing numerical and graphical