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exceptional postdoctoral research fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g
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evaluating them on real-world data (e.g., in iCAN ) in collaboration with precision cancer medicine experts. We strive to revolutionize cancer care through the power of massive data and machine learning! Key
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and implement innovative image analysis methods to quantify plant characteristics. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and
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Lightweight and flexible solar cells Space deployable structures Device analysis in space environments Big data, AI, and machine learning for space solar initiatives Outstanding Postdoctoral Training Strategy
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, analysis, and optimization. The goal of the project is to develop methods for the synthesis and analysis of systems producing renewable fuels and chemicals; and use these methods, in collaboration with other
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: Learning Agility: Ability to learn new procedures, technologies, and protocols, and adapt to changing priorities and work demands. Teamwork: Ability to work collaboratively with others and contribute to a
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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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to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience. The ideal candidate is self-motivated and can work
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | 4 days ago
. For those interested, there is also an option to learn and use in vivo cancer or diabetes models. Collaborative and Supportive Environment: You will collaborate closely with experienced scientists and
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into concrete observational and computational strategies. This work will be carried out under the supervision of Professor Alessandra Corsi, in close connection with collaborators at the JHU’s Institute for Data