Job Description

Job Description:

The Department of Computer Science seeks to hire a postdoctoral fellow in the areas of Machine Learning and image fusion techniques for a project studying land cover change in Amazonian rain forests due to illegal and informal gold mining activities. The position starts in January 2021, and the successful candidate will also be a fellow at the Wake Forest Center for Energy, Environment, and Sustainability. Our research centers on 1) developing deep learning methods for multi-data fusion for detecting and quantifying land cover and land use change over large areas by incorporating sources of moderate optical, synthetic aperture radar, VHR optical, and HSR optical data, 2) developing transfer learning techniques to transfer extracted features from Unmanned Aerial Vehicle data to enhance satellite imagery, and 3) using spatial
and temporal multi-source remotely sensed datasets to generate super-resolved deforestation maps using RNN. In addition to advancing scientific frontiers, results generated by this project will have immediate policy relevance.

We are looking for candidates who are enthusiastic about using AI research to

address environmental issues, self-motivated, and determined to bridge the gap between the theory and practice of DL and CV algorithms. The successful applicant must have a Ph.D. in the fields of Computer Science, Computer Engineering, Statistics, or related fields. Candidates are required to have a strong background in computer vision and deep learning. The candidates must be fluent in written/spoken English and good at presenting research accomplishments.

The successful candidate will collaboratively prepare presentations and papers for
dissemination of results and help train graduate and undergraduate student research
assistants. This is a two years position.

Candidates should submit a cover letter explaining interests and goals with respect to research, a complete CV, 2-3 references that could provide a letter of support, and 2 relevant publications. Contact Sarra Alqahtani (alqahtas@wfu.edu) with questions.