MS/PhD Position, Turfgrass Weed Science, Agronomy Department, University of Florida in Gainesville, FL with Agronomy Department, University of Florida in Gainesville, FL

Successful candidate will conduct research in the area of weed management, herbicide efficacy, physiology, resistance, as well as target weed biology and desired crop safety in various warm-season turfgrass systems.

Required Qualifications

The candidate is required to possess:

• Bachelor of Science or Master of Science degree in turfgrass science, weed science, plant sciences, agriculture, agronomy, horticulture, or closely related fields.
• Excellent oral and written communication skills (non-native English speakers please refer to https://admissions.ufl.edu/apply/graduate/international for TOEFL/IELTS requirements).
• Computer literate fluency including proficient use of spreadsheet and word processing software (MS Office).
• Experience in statistical data analysis using appropriate software (e.g., R; PhD level applicants).
• Capacity to work outdoors in field conditions.

This position requires willingness to work with pesticides on a daily basis. The incumbent must possess (or obtain within three months of joining) a valid pesticide applicator’s license from the State of Florida. Travel to remote research locations will be required; thus, a valid driver’s license is mandatory (or must be obtained within three months of joining).

Succesfull candidate is expected to demonstrate strong work ethics, remarkable previous academic performance, exceptional organizational and time management skills, attention to details, creativity, problem-solving and independence.

Preferred Qualifications / Skills Considered as Assets

Previous experience in conducting field, greenhouse or lab research, especially related to the areas of turfgrass science and/or weed science is preferred. Both, the knowledge of generally accepted in the field of turfgrass weed science methods and experience with the use and maintenance of pesticide application equipment (preferably in research settings) will be considered an advantage. Moreover, experience in data modelling or machine learning will be considered as a valuable asset.