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light/heating modules, and selection and sorting routines. Guided by machine learning, we will perform directed evolution experiments where we optimize the synthetic genome that encodes for a biological
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Pneumatic Tires, Structure-Process-Properties Relationships. As part of it, we are currently looking for a postdoc on machine learning for road characterization. How will you contribute? Do you have proven
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advanced machine learning and deep learning tools to decode the complexity of immune–tumor interactions, integrate multi-omics data at scale, and predict patient responses to therapy. The center works at
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engineering, or a related field. · Strong background in machine learning or data analytics and hands-on experience handling big data. · At least one year of research experience in transportation
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R or equivalent skills in another relevant language. We are not expecting you to be an expert in all forms of computer simulation, Large Language Models, or machine learning etc, but a working
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of efficient and robust neural networks. About your role: Independent research in the area of mathematics of machine learning, focusing on the development as well as the analysis of different algorithms and
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biogeochemical model using times series forecasting and machine learning. The Post Doc will focus on one or two of the questions depending on their expertise and interest. Minimum Acceptable Education & Experience
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profound difference Frank Rhodes has made at Cornell by furthering scholarship and research in areas related to poverty alleviation, support for the elderly and disadvantaged children and youth, public
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or experience in nontraditional research publication methods and collaborative notetaking software (e.g., Roam Research, Obsidian, Notion). ? Familiarity with cloud computing and machine learning techniques
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assimilation and machine-learning techniques, (b) process understanding of the neighborhood risk of heat waves and fires associated with the change of weather pattern, and (c) novel algorithm development