115 condition-monitoring-machine-learning Postdoctoral positions at University of Washington
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status, pregnancy, genetic information, gender identity or expression, age, disability, or protected veteran status. Benefits Information A summary of benefits associated with this title/rank can be found
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in language development across cultures. The team studies how infants and young children learn one, two, or multiple languages using a variety of methods, but with an emphasis on long-form recordings
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interpreting wet-lab synthesis data are encouraged to apply and will have opportunities to explore machine learning-guided approaches in chemistry. In addition to excellent research skills, we are seeking
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uterus’s electrical maturation and mechanical changes. The goal of the lab is to develop tools to monitor pregnancy and labor progression and assess the effectiveness of treatment strategies to manage labor
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intellectual property generated in University laboratories remain the property of the University. Mentorship of trainees in the lab. Working Conditions: This position works in a laboratory environment with
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regard to race, color, creed, religion, national origin, sex, sexual orientation, marital status, pregnancy, genetic information, gender identity or expression, age, disability, or protected veteran status
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the geography of outdoor activities and psychological stress. Duties/Responsibilities The researchers will contribute specifically through: Gathering data, developing and implementing machine learning models, and
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analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job Description Primary Duties
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the Department of Speech and Hearing Sciences and Institute for Learning & Brain Sciences University of Washington: Academic Personnel: College of Arts and Sciences: NATURAL SCIENCES DIVISION: Speech & Hearing
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. Experience with high-throughput molecular biology assays. Experience with complex functional experiments. Background in machine learning, AI, or data integration for genomic datasets. Familiarity with gene