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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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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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, and develops various tools for bioimage analysis, mostly using machine learning and AI-based models. As a postdoc you will conduct research using various methods in cell-and molecular biology, but
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, aiming to create automated and AI-assisted microscopy workflows for large-scale human cell imaging and modeling. You will be responsible for developing and optimizing wet-lab and imaging protocols
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and diseased human tissues and immune cells of the heart, cardiovascular system, and gastrointestinal tract. The research will build spatiotemporal models of human immunological tissue architectures