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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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Comprehensive skills in data analysis and bioinformatics Proficiency in programming with Python Proficiency in version control (Git, GitHub) Meriting criteria are: Experience in mechanistic and ODE-based
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A position as Associate Senior Lecturer/Assistant Professor (tenure track) within the area Data-Driven Evolution and Biodiversity in aquatic or terrestrial environments is open at the Swedish
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position as Associate Senior Lecturer. Data-driven precision medicine and diagnostics includes computational analysis that integrates molecular and clinical data for precision medicine and diagnostics
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acquisition and data analysis infrastructure. Duties We are looking for a researcher with strong interest in developing, implementing and adjusting image analysis tools for quantitative analysis of microscopy
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with nano-LCs. Performing data analysis and interpretation using established proteomics software and tools, such as Proteome Discoverer, Spectronaut, Skyline, Byonic, Perseus and R-based Shiny
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microbial ecology. The research could also address advanced data-driven and machine-learning approaches for the analysis and integration of complex neural and movement data, supporting new insights
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focus in multidisciplinary research. The CMCB laboratory aims more specifically at developing cutting-edge data/image analysis as well as modelling strategies to answer fundamental biology issues with
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. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in time and space, how this affects
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learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include: Development of ML/DL methods for multi-omics data analysis. Design and implementation