202 machine-learning-"https:"-"https:"-"https:"-"https:"-"The-Open-University" positions at Zintellect in United States
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background in computational biology, modeling or Machine Learning and Artificial Intelligence Familiarity with basic techniques and principles in cell and molecular biology and biochemistry Willing to learn
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avian influenza (HPAI) airborne transmission between U.S. poultry facilities. The primary focus of this opportunity will be learning to develop statistical and mathematical models to assess airborne
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of the Agency. Learning Objectives: Under the guidance of the mentor, you will receive training in pharmaceutical science, laws and regulations related to pharmaceutical quality, lifecycle management of drug
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selection programs. Learning Objectives: By the end of this training/research experience, the fellow will be able to: Explain the structure and functional organization of the bovine genome and describe how
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JW, Surov SS, Liang Y, Parunov LA, Ovanesov MV. Effect of pH on thrombin activity measured by calibrated automated thrombinography. Res Pract Thromb Haemost. 2020 Jun 12;4(5):944-945. Learning
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filtration research techniques, and data analysis. Opportunities for active participation include: Experimental: You will learn how to engage in 3D printing of novel filtration devices and the testing
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necessary to become credentialed as a Principal Investigator Applying a broad range of statistical and machine learning methods to human performance data collected in real-world settings Developing
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high school seniors, who are pursuing undergraduate studies in STEM, with an opportunity to explore the world of agricultural science through hands-on learning experiences. Participants will have the
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accurate image labeling and annotation to support supervised machine learning applications. Prepare and gain experience through field experiments, including protocol development, equipment setup, and data
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shifts and geochemical fluxes within the vadose and groundwater zones. Learning Objectives: This summer program provides an opportunity to gain hands-on experience with Forest Service monitoring protocols