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Field
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personalized medicine. By integrating human cohorts, single-cell multi-omics technologies, and expertise in systems and computational biology, we generate high-profile research that bridges big data with
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from a range of stakeholders (e.g. NOAA, ICCAT, DFO). The PDRA will work within the Marine Ecology Research Lab, a large lab consisting of four faculty, two PDRAs, five PhD students, two Masters
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, assessment of images, or assessment of memes. Preference for those with experience working with large scale data, millions of actors and messages. Successful candidates will work on at least two related agent
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: Appropriate PhD in related field Knowledge, Skills, and Abilities: Familiarity with appropriate laboratory and technical equipment; ability to effectively use a computer and applicable software to create data
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collaborative research on immune development and responses to respiratory viruses. Analyze clinical and large-scale datasets (e.g., genomic, transcriptomic, proteomic data). Design and execute experiments using
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language models that can democratize access to reliable health information. Applicants will collaborate closely with PI Emma Pierson as well as other PhD students and postdocs in the lab. Union: https
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cooperative working relationships with coworkers, students and collaborators. Organizational skills to effectively manage and maintain large data sets. High level of independence and reliability. Demonstrated
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learning, and signal processing applied to conduct high quality research with a particular focus on neuropsychiatric and cardiovascular diseases. Working in a large team of 15 PhD students, several postdocs
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publications Work effectively in a large, multidisciplinary, multi-institutional team of scientists and engineers. Basic Qualifications Must be a recent (within the last three years) PhD graduate in Biochemistry
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visual representation and analysis of large-scale 2D/3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems Section, Computer Science and Mathematics Division