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unique D-MIMO testbed at Lund University, extending existing and creating new deep learning-based models for anomaly detection, theoretical and numerical studies of detection quality, creating new
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
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perturbation-based GRN inference for single-cell and spatial multi-omics data, to boost GRN quality and add the cell type and tissue heterogeneity dimensions to causal regulatory analysis. A deep learning
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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or