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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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applications. Project description This PhD project focuses on advancing the field of multi-modal data analysis and generation, integrating computer vision, natural language processing (NLP), and machine learning
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machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid understanding of fluid dynamics and heat transfer, as
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identify systems-level mechanisms in cancer that can be used to uncover new biomarkers, drug targets, and paths to drug resistance. The long-term goal of our lab is to enable computer-aided design of
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qualifications and merits for the position are: • Knowledge and experience on image processing or computer vision • Knowledge and experience on generative AI • Knowledge of data driven methods for modelling and
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understanding of the clinical aspects of breast cancer to assess the performance and relevance of the integrated data. Advanced computational methods, including Artificial Intelligence and machine learning, will
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Laboratory is a growing interdisciplinary research group doing basic and applied research at the intersection of cybersecurity, control theory, and machine learning. Our vision is to develop methodologies
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information from sequence alone, independent from optical microscopy. This adjacency or neighbor-neighbor information can be used generate images in a computer. Today one person can routinely read millions
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and computer science and be fluent in oral and written English. Specific depth in mathematics, computer security or encryption is valuable but not a requirement. It is an advantage if you have previous
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traditional statistical modelling and supervised machine learning to quantitatively develop and internally validate the risk calculator. Linked nationwide registry data from more than 70,000 individuals