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to the development of innovative solutions to address novel global ecological and conservation challenges. This position will also collaborate closely with the CAPS Conservation Science Program to translate research
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Qualifications: Bachelor's degree in computer science with a minimum of five (5) years of desktop support and administration for PC's/Mac's. Experience with hardware/software support in a computer lab environment
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, stakeholders, local officials, county employees, community groups and service providers. The Farm Director shall recommend opportunities for program growth and adaptation and identify additional sources
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, stakeholders, local officials, county employees, community groups and service providers. The Farm Director shall recommend opportunities for program growth and adaptation and identify additional sources
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opportunities, conferences and workshops appropriate to program and personal skill enhancement. File professional improvement plan and constructively evaluate self-performance for improvement and subject matter
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professional development opportunities, conferences and workshops appropriate to program and personal skill enhancement. File professional improvement plan and constructively evaluate self-performance
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The Energy and Environment Program Leader will provide overall, high-level program and administrative leadership to assess, develop, deliver, and evaluate programming for the Energy & Environment
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, and enhance program visibility. This dual role is instrumental in shaping the composition of incoming graduate cohorts and increasing career opportunities for students and alumni. Serving as the primary
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-Track Assistant Professor - School of Applied and Engineering Physics The School of Applied and Engineering Physics (AEP) at Cornell University seeks applications for tenure-track Assistant Professor. We
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around the world. Position Function The Department of Earth and Atmospheric Sciences at Cornell University is seeking a Senior Research Associate to lead an innovative research program in AI, Deep Learning