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trials and deep learning-based modeling of multimodal biomedical data. We are seeking two highly motivated scientists with expertise in Causal Inference / Statistics / Deep Learning / Bioinformatics
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-based methodologies for protein structure prediction and cofolding Machine learning guided virtual screening Taking on organizational tasks and writing grant applications Your Profile The ideal
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, for event reconstruction and classification, including potentially machine learning/AI Interpretation in suitable theoretical models Contribution to software activities that are required for wider use by DESY
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for working in a competitive interdisciplinary research team are essential. Potential scientific projects will use computational neuroscience methods such as dimensionality reduction, machine learning
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Responsibilities Conduct research in computational methods for environmental and engineering applications. Develop and analyze numerical algorithms, reduced-order models, and machine-learning-enhanced simulation
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Max Planck Institute of Molecular Cell Biology and Genetics, Dresden | Dresden, Sachsen | Germany | 3 months ago
. Experience in machine learning algorithms and tools. Experience in microscopy and image analysis. Effective collaboration in interdisciplinary teams. Strong analytical reasoning and persistence in experimental
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spatial omics data and to uncover the regulatory mechanisms driving cellular differentiation during vertebrate embryonic development. In this project, you will develop quantitative machine learning models
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Computer Science or Mathematics, ideally with a background in one or more of the following areas: Optimization, Game Theory, Machine Learning Applicants must demonstrate: • An excellent academic record, including
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM
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Hamburg, ESRF in Grenoble and others) analysis of the experimental data, ideally connecting to our machine learning tools presentation of scientific results on conferences and in publications supervision