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) Qualifications Essential Qualifications PhD in forestry or closely related field such as land resources Preferred Qualifications Have experience with bark beetles, tree defenses, and climate data Have experience
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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job Duties: 1) Conduct research and provide technical assistance to the PI, students, and trainees on data analyses related
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. Applications including the following (please provide in a single PDF): Cover letter Curriculum vitae Brief description of research interests Reference list with contact information In addition, applicants should
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Materials: • CV • Cover letter explaining qualifications • Contact information of at least 3 references are required Diversity The University recognizes and values the importance of diversity and
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aspects of immunity. Pay and Benefits Pay Range: $61,008 to $70,344; depending on education/qualifications/experience Please visit the Benefits for Postdoctoral Candidates website for more information
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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of research. -Carry out and/or supervise laboratory experiments. -Develop experimental procedures. -Collect and analyze data. Qualifications Required Qualifications: - Ph.D. or an equivalent degree. Preferred
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-dimensional/multicolor panel design, immune phenotyping, and data analysis • Skilled in molecular biology techniques including RNA/DNA isolation, qPCR, ELISA, gene knockdown, and magnetic-assisted cell sorting
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flexibility in schedule and/or work location. Please note that 100% remote work requires approval prior to offer. Job Responsibilities Data management and analysis (40%) Establish data management infrastructure
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implementation of various projects, including (but not limited to) scaling, algorithm development, scaled score development and documentation. Support the creation, management, and retention of large-scale data