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Methods Clin Dev. 10: 105-112, 2018. Learning Objectives: Under the guidance of a mentor, you will receive training in cutting edge technologies directly relevant to promoting public health, opportunities
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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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to revolutionize agriculture in Morocco by combining cutting-edge technologies, including crop growth models, remote sensing data, data assimilation, machine learning, and seasonal weather forecasts. As a
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needs education and five special education majors (speech and language therapy, hearing, language and communication, specific learning difficulties, psycho-social difficulties, and neurodevelopmental
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colleagues who teach and research in the other subject areas that Regent’s offers. The closing date for applications is Monday 27 April (9am). Please click the 'Apply' button above for the further particulars
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. The DBNRRC conducts nationally significant research to enhance the productivity, sustainability, and health benefits of U.S. rice production, and this fellowship is designed for a fellow interested in learning
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community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals
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Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | 18 days ago
will generate critical safety and efficacy data to inform regulatory evaluation of these emerging therapeutic modalities. Learning Objectives: If selected, you will have the opportunity to: Develop
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studying for a doctoral degree in the arts, science, law, commerce or agriculture. Availability Available to New Zealand Citizen or Permanent Resident Students Our website uses tracking technologies to learn
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postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health, including experimental design and reinforcement