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integration (up to 3 million cells) using deep learning-based approaches, hierarchical clustering, and cell type annotation benchmarked against published CRC atlases Deconvolution and TME characterization
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science or biostatistics Ability to work independently and collaboratively Meritorious qualifications: Experience with machine learning or deep learning Experience with computer vision or image analysis Experience with 3D
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collaboratively Meritorious qualifications: Experience with machine learning or deep learning Experience with computer vision or image analysis Experience with 3D data, geometric representations, or parametric
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are essential Additional qualifications Experience and courses in one or more subjects are valued: statistical machine learning, optimization, deep learning and signal processing. Rules governing PhD students
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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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Initiatives in Forest Research (WIFORCE) program. The successful applicant will work on the development of bioacoustic monitoring methods using automated recording units (ARUs), deep learning methods, and
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++ or similar) and an interest in quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not
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. Additional qualifications Experience with one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models
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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