Senior Research Fellow: Spatiotemporal Population, Demographic, & Environmental Modeler


University of Louisville


Monday, January 15, 2024

Start Date

Monday, January 15, 2024

Job Description

We are seeking to recruit a Post-Doctoral Research Fellow for a newly funded NASA project, “Synthesis Study of Land Cover, Land Use, and Demographic Change under Multi-Dimensional Developments and Climate Pressures in Southeast Asia,” to supplement expertise in earth-observation processing, sociodemographic modeling and cloud computing integration and synthesize understanding of the dynamics and interactions of the complex changes in the biophysical and socioeconomic systems along the rural-urban continuum. The work will directly inform scientific support to the strategic development of proactive policies that are regionally relevant and effective in Southeast Asia. The position would be based in the Department of Geographic & Environmental Sciences at the University of Louisville but will involve substantial collaboration and engagement with partner institutions on the project. Remote, off-campus applicants may be considered if exceptionally qualified.

The position requires expertise in socio-ecological modeling and synthesis to integrate various Earth Observation layers with high resolution mapping of population distributions and demographics based on census, household survey and high-resolution covariate data layers. The work will involve collaborations with US-based partners at CUNY, NASA JPL, Columbia, and Western Michigan University, as well as international institutions to provide a consistent and responsible point-of-contact for across partners. By joining our vibrant and well-connected interdisciplinary team the candidate will have opportunities to lead career-changing, high-impact publications for the project, as well as conduct independent research that aligns with their own interests and background.

The project work to be undertaken by the candidate will involve:

(i) Modification and documentation of existing prototypes for statistical and programming interfaces for data processing and final product generation
(ii) Contributing to the design of population synthesis mapping methods with the integration of novel urban and built-area datasets and urban growth models along SE Asian rural urban continuums
(iii) Together with established colleagues at collaborating institutions be a primary facilitator for technical- and production-related exchanges between U.S., Asian, and European collaborators and stakeholders
Other job responsibilities will include extended travel to domestic partners and Southeast Asia for meetings and project coordination.

Given these tasks, strong experience in spatial statistical analysis, computer programming, social-ecological systems modeling, and/or demography is therefore a significant advantage. Applicants should have a Ph.D. in a statistical/computational/quantitative discipline, or relevant industry experience. The current tools used by the project team include R, Python, Stan and its implementations, the Google Earth Engine API and ESRI platforms and applicants with experience in these environments will be preferred. The project work will be highly interdisciplinary and as such there is some flexibility to accommodate expertise from a range of cognate disciplinary backgrounds (e.g. geography, demography, statistics, computer science, ecology, epidemiology etc). Ability and willingness to travel overseas is also necessary.

The candidate will work under the supervision of Dr. Forrest Stevens and Dr. Andrea Gaughan at the University of Louisville trained in interdisciplinary, socio-environmental system thinking, and have the opportunity for continuing to develop independent research ideas. The position starting date is January 15, 2024 for a period of 1.75 years and will remain open until a suitable candidate is found. Informal enquiries may be made to either Dr. Forrest Stevens ( or Dr. Gaughan ( or Applications must be made online at for Job ID 33110. Application materials must be uploaded in a single document.