Drs. Huang and Vazquez-Prokopec at the Department of Environmental Sciences, Emory University, are seeking an outstanding and highly motivated postdoctoral researcher whose interests focus on the application of image processing and deep learning algorithms to environmental problems. The successful applicant will join a multidisciplinary team of Geographers, Entomologists, and Ecologists investigating how emerging AI tools can be leveraged to improve the surveillance and control of urban malaria in Ethiopia. Within this new 3-year project, the postdoc is expected to design and conduct research while receiving advanced training from the Principal Investigators, thereby enhancing professional skills and research independence needed for a successful scientific career. Postdoctoral positions are temporary research appointments, initially for one year, with renewal contingent on satisfactory progress and available funding. The starting salary for this position will be $65,000 and will follow NIH pay scale for postdoctoral researchers.

Additional Information about the position:

The Postdoctoral Researcher will join “NextGen-LSM”, an innovative project recently funded by the Gates Foundation. The project aims to develop rapid, efficient, Larval Source Management (LSM) strategies for urban centers in Ethiopia, leveraging remote sensing, advanced machine learning, and web-based tools. A major component of the project centers in the application of AI/ML algorithms for efficient processing of remotely sensed data (very high spatial resolution satellite and UAV imagery) with the goal of detecting key water habitats used by the mosquito Anopheles stephensi, a recent invader of Africa that is implicated in the increase in urban malaria cases in Ethiopia. By automating the detection of mosquito habitats from remotely sensed data, the project aims to guide the application of long-lasting larvicides to control An. stephensi and malaria transmission in selected urban environments from Ethiopia.

Key Responsibilities

• AI Model Development: Create and deploy cutting-edge machine learning/deep learning models to identify urban mosquito habitat sites using ultra high-resolution satellite and UAV imagery.
• Imagery Analysis: Derive relevant environmental indicators (e.g., water quality metrics) and landscape confounders (e.g., tree shade, distance towards roads, etc.) from remote sensing data to support the identification of super-producing mosquito habitats.
• Data-Driven Inference. Integrate multi-source data to map, predict, and prioritize potential high-yield breeding sites in urban landscapes.

Preferred Qualifications:

• A Ph.D. in Geography, Environmental Sciences, Computer Science, Engineering, or a related field awarded before the start date.
• Demonstrated expertise in image processing and deep learning algorithms.
• Proficiency in Python, R, and or coding languages
• Familiarity with UAV imagery handling and deep learning model deployment;

Application materials

• Curriculum vitae
• Letter of application/Cover letter (2 pages max)
• Research statement (2 pages max) 
• Writing samples (a maximum of three; this may include journal articles, books, book chapters, dissertation chapters, and coding examples etc.)
• A list of two references with telephone numbers and email addresses.  

Important Dates:

Application deadline: 02/28/2025

Project starting date: 04/01/2025

Project duration: Three years

How to Apply:

To apply, contact Kim Awbrey (Coordinator of International Projects) at kim.awbrey@emory.edu and include all the requested materials. Application screening will continue until a suitable candidate is found.  

Emory University is an equal employment opportunity and affirmative action employer. Women, minorities, people with disabilities, and veterans are strongly encouraged to apply.

Further information

Emory University’s Department of Environmental Sciences and College of Arts and Sciences.