41 linked-data-"https:"-"https:"-"https:"-"https:"-"INSA-RENNES" Postdoctoral positions at Texas A&M University
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materials, participation in offshore research cruises and onshore training workshops, and conducting geoarchaeological fieldwork and data interpretation. This role is a unique opportunity to contribute to all
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using a stable light-isotope-ratio mass spectrometer (IRMS), and model experimental data. Responsibilities also include preparing manuscripts based on research findings and performing routine maintenance
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: developing/performing/troubleshooting immunoassays, determining the function of selected genes in Streptococcus equi including in vitro and in vivo studies, generating/interpreting data and preparing data
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records and contribute to laboratory data management and validation documentation. Potential field work and international travel. Managing data pipelines, ensuring data integrity, and leading quantitative
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, or Astrophysics. A well-qualified candidate for this position will also possess: Experience in astronomical data reduction and analysis, astrophysical theory, or astronomical instrumentation. Excellent verbal and
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projects in their field of expertise. Assists with the preparation and cleaning of worksite. Analyzes research data and summarizes results. Writes and may contribute to research papers, articles, and
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on the application under the CV/Resume section. Responsibilities: Research Successfully conduct and perform specified experiments and techniques. Maintain electronic data base with experimental protocols and
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(cannula, optical fiber, or electrode). Analyze electrophysiology and photometry data using appropriate software and statistical methods. Conduct selected behavioral assays (e.g., operant self-administration
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applicable. Preferred Qualifications PhD in data science, and/or public health or related fields including health services research, health informatics, computer science. Experience in data analysis using
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Job Description Here's a Glimpse of the Job The Postdoctoral Research Associate will be involved in developing deep learning architecture for multi-object data integration, federated learning approaches