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self-correction capabilities of the developing nervous system, focusing on plasticity in cell behavior and cell-cell interactions during regrowth and repair of tissue organization. The work will be
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chronic autoimmune diseases of the nervous system. In this project, transcriptome data will be analyzed using state-of-the-art cell-based transcriptomics methods (single-cell RNAseq, single-nucleus RNAseq
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users: provide project consultation, training and hands-on support, schedule and prioritize measurements, and ensure timely data delivery Drive method development for single-cell MSI and integration with
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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, initiative/commitment, ability to work in a team and willingness to cooperate, willingness to learn We offer: Interdisciplinary research at the interface of politics, economics and society Work in national and
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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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variants on human traits and single-cell readouts. Our research group is pioneering computational methods for deciphering molecular variation across individuals, space, and time. We have a track record in
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areas is expected: numerical analysis, scientific computing, model reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming
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to the development of personalized microbiome-based strategies to aid in the detection, monitoring, treatment, and prevention of human diseases (e.g., Li et al., Nature Metabolism, 2024; Ni et al., Cell Metabolism
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling