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values of the ILR School, and for monitoring quality, impact, and outcomes of education, training, technical assistance, consulting and research activities of the program. Our Team: The Climate Jobs
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strategies to expand production and profitability. Other responsibilities include: Planning and implementing educational programs utilizing various methods, including direct teaching through group experiences
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Diploma or Equivalent Education. Six (6) months volunteer or work-related experience. Valid NYS Driver’s License. Ability to clearly communicate and to read and write in English. Basic Computer Skills
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, and sustainability. 3 weeks of paid vacation 13 additional holiday days with 2 floating holidays to be used at your discretion An award-winning employer provided benefits program Comprehensive health
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, and sustainability. 3 weeks of paid vacation 13 additional holiday days with 2 floating holidays to be used at your discretion An award-winning employer provided benefits program Comprehensive health
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-reaching extension network and the college’s translational research methods, we deliver our findings directly to communities and families, ensuring that our work reaches those who need it most. We prioritize
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education program. The university is committed to excellence in academics and athletics, gender equity and diversity in its programs, and a well-balanced, broad-based intercollegiate athletics program
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data for reporting and program improvement purposes using standard, established policy, procedure and methods. Assist with making recommendations for improvement/changes to strengthen and improve
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Two Faculty Positions Available in the Department of Statistics and Data Science, Cornell University
mathematical statistics, computational statistics, and machine learning to the development of statistical methods for astrophysics, ecology, economics, epidemiology, financial modeling, genomics, high
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Labor Relations (ILR). Specializations in the department range from mathematical statistics, computational statistics, and machine learning to the development of statistical methods for astrophysics