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to enhance imaging quality and speed Creating efficient data acquisition and processing workflows for large datasets of skin nanotexture images Optimizing hardware-software integration for robust performance
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)function. Our approach develops methods to interpret high-performing deep learning models to distill knowledge that they learn from big, noisy biological sequence data. Our goal is to elucidate biological
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within the Department of Radiology. The candidate will have the opportunity to work on “big data” studies in health and diseases, including schizophrenia, psychosis, and Alzheimer’s disease. We collaborate
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Innovation . The application must be in English and include a curriculum vitae, degree certificate, a complete list of publications, a statement of future research plans and information about research
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, degree certificate, a complete list of publications, a statement of future research plans and information about research activities, teaching portfolio and verified information on previous teaching
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to integrate large and complex molecular datasets with information at individual level, with specific attention to open and reproducible research, e.g., in the development of codes and algorithms. We will focus
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research is based on large and high-dimensional datasets across multiple modalities, including molecular, clinical and histopathology imaging data. Our computational pathology research is based
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-engineering. A semantic interoperability backbone and data management are also required due to the large number of stakeholders and their need to receive or exchange information. Experience with modelling
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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carry out field studies, collecting and analyzing large data sets. Use and evaluate AI tools to support research and training. Create engaging, science-based training materials in both English and Spanish