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model and biological membranes. The experimental data will be paired with results from molecular dynamics simulations to provide a complete characterization of the biophysical properties of the imaged
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Transformer-Based Foundation Model for DNA Methylation in Longitudinal Cohorts.” The focus is on developing next-generation AI models for the analysis of DNA methylation. Using longitudinal data from, among
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. You would be welcomed in the the Yant Lab (https://www.yantlab.net/ ) Using large-scale graph-based pangenomics and forward evolutionary simulations, the student will develop predictive models
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recruiting an outstanding and ambitious postdoctoral researcher in computational biology to advance the integration and modeling of large-scale microscopy data using modern machine learning approaches
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longitudinal cohorts, SATSA and Betula, integrating established dementia biomarkers with inflammatory, metabolic, and genetic data using advanced statistical modeling and data-driven methods. The doctoral
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annotation, integration, and modelling application of statistical and mathematical modelling approaches to understand and predict immune cell fate decisions identify transcription factor combinations
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to effectively train AI models based on limited and imprecise information as well as unbalanced and heterogeneous multimodal data. This needs-driven method development finds direct application in healthcare
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of the largest pan-cancer signaling models in the literature. SPARCED is compatible with high-performance and cloud computing, can simulate thousands to millions of single-cell trajectories, is easily expandable
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, perform simulation studies, and apply developed methods to empirical datasets. The positions do not involve any lab work. The work includes mathematical modeling, algorithm development, statistical analysis
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, providing a platform to identify and optimize therapeutic candidates. Our group specializes in developing technologies to assess the multicellular environment within three-dimensional microtumor models