"Research Scientist - Department of Ecology, Evolution and Behavior"

We seek a researcher with extensive experience in plant disease ecology, imaging spectroscopy, remote sensing, and machine and deep learning approaches to work in the detection of invasive diseases using spaceborne observations. We are interested in recruiting an individual with strong theoretical and modeling skills, with a strong background in using satellite data to construct predictive models that allow the early detection of tree diseases from space. The research scientist will be responsible for 1) managing (accessing and processing) Landsat and Sentinel-2 data, 2) developing predictive models for the early detection of tree diseases, and 3) writing manuscripts to communicate the results.

Percentage breakdown of duties: 60% Conduct time series analysis of land surface phenology (LSP) based on Landsat and Sentinel-2 imagery. Develop and test predictive models of disease detection. 35% Contribute to manuscript preparation and reports. 5% Other duties, including preparing and delivering presentations, conducting outreach with the public.

Required Qualifications: Ph.D. in biodiversity informatics, remote sensing of biodiversity, or related fields, and a strong background in implementing advanced machine learning and deep learning approaches, spatial statistics, and experience in quantitative ecology and applied statistics. Analytical and programming skills commensurate with Ph.D. level training. Demonstrated ability to conceptualize, write, and publish high-quality scientific work.

Preferred Qualifications: Experience working with large data sets (Big data). Experience with Python, Julia, R, C++, and/or other programming languages. Experience with machine and deep learning modeling approaches and developing Bayesian models. Multidisciplinary skills to bridge fields such as plant disease ecology, remote sensing data, and geospatial statistics. Respect for and sensitivity to diverse cultures, identities, and backgrounds in the workplace.