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health trajectories from big health record data using advanced AI methods Professor Yue Li is hiring one Postdoc for the machine learning and AI research in healthcare. The position will be held
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machine learning components into the IMAGE server architecture to enable the team’s designers and developers to create compelling end-user experiences on the Monarch. Key responsibilities include
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for data processing, statistical analysis, and machine learning. The Candidate will engage in research, applying these methods to MEG and EEG data to explore their relationship with structural brain changes
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the features in our statement of work. You will primarily work with the existing development lead to partition the work based on your skills and interests. Key areas include: Work with lead developer and machine
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sensing instrumentation for clouds and precipitation (e.g., cloud radar, passive or active satellite sensors) Experience with instrument simulators. Experience with machine learning techniques. Strong
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of conversational prompts to younger and older adults. Use machine learning tools to help design the CUI in accord with the study principles. Execute experiments testing older and younger adult participants with
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organizational and time management skills, attention to detail, ability to manage a complex agendas, • Excellent computer skills with knowledge of a variety of software (e.g. Microsoft Word, Excel, Outlook, etc
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-solving skills • Willingness to acquire new skills and adapt to new situations • Professional disposition, possess a high degree of efficiency and self-motivation, and have strong work-ethics and respect
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approaches and tools, • Experience analyzing large-scale genomic data (including next-generation sequencing and/or genome-wide genotyping datasets), • Strong computer programming skills (R/Python preferred
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++ programming, data collection, and the use of advanced technologies. Perform data analysis using relevant Machine Learning (ML) tools such as Pytorch, interpret results, and present findings to the research team