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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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–based studies for protein quantification and comparative analysis, to specialized applications in glycomics and glycoproteomics. We use Orbitrap and timsTOF mass spectrometers interfaced with LC systems
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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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credits, or Mandatory requirement for English equivalent to English B/6 Experience in formulating lipid nanoparticles with mRNA via microfluidics Experience in sample preparation and data analysis from
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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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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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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’ system engineers. Train and support users in sample preparation and data collection (both for siungle particle and tomography). Provide advice and hands-on guidance in data processing and analysis, helping
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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
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. Forest Mycology and Plant Pathology ), inventory and analysis of terrestrial resources and the environment (Dept. of Forest Resource Management ), aquatic biodiversity, microbial ecology and environmental