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Field
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to implement and optimize AI/ML models for biomedical datasets. Preferred Knowledge, Skills and Abilities Mathematical Modeling: Strong foundation in numerical modeling, graph theory, and statistics. Algorithm
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for excellent scientists with background and experience in one or more of the following areas: graph algorithms, parameterized complexity, approximation algorithms, extremal combinatorics, structural graph theory
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knowledge graphs and multimodal embeddings for cancer patient digital twin construction. Lead and co-author high-impact publications and grant proposals. Collaborate with clinicians, bioinformaticians, and
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facilities and industry networking opportunities. Key responsibilities will include: Coordinate data collection and analysis, preparation of graphs, figures, and manuscripts for conference presentations and
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results details of experiments for publication in scientific journals or presentation to scientific conferences Participating in the development of figures, tables, and graphs. Where will I be located
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receipts of proposals, and maintaining a system to track proposals. Evaluate and perform preliminary analysis of the data using graphs, charts or tables to highlight the key points of the research results
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for cancer research and diagnosis as well as graph neural networks for microscopy. Main responsibilities The position involves taking an active part in CMCB lab’s daily research. The succesful postdoc will be
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in the following areas: Deep Learning, Scientific Machine Learning, Stochastjc Gradiant Descent Method, and Numerical PDE’s - Advised by Dr. Yanzhao Cao Probabilistic Graph Theory (Network Traversal
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using graphs, charts or tables to highlight the key points of the research results collected in accordance with the research protocols as stipulated. Prepare and present presentations regarding research
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improve health and wellbeing • Strong quantitative skills in data analysis using R, Stata, Python or similar software and ability to summarise results using clear, concise and relevant tables and graphs