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The Asl Research Group in the Department of Political Science and Geography is seeking a motivated undergraduate research assistant to join a summer research project beginning June 1, focused on climate hazards, Nature-based Solutions (NbS), and AI-driven spatial modeling in coastal Virginia. The selected student will support research activities using datasets on NbS and heat exposure, including data organization and cleaning, GIS-based mapping and spatial analysis (e.g., flood and heat exposure layers), and assistance with data integration and standardization. Additional responsibilities include supporting data visualization and documentation, as well as assisting with stakeholder engagement activities such as workshop preparation and feedback collection. This position offers valuable hands-on experience with interdisciplinary research methods in geospatial analysis and environmental data science and includes a $6,000 summer stipend.

Interested applicants should submit a one-page statement outlining their interests, relevant skills, and career goals, along with a current CV, no later than April 15, to Dr. Sina Razzaghi Asl at srazzagh@odu.edu.
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The Asl Research Group in the Department of Political Science and Geography is seeking a highly motivated master’s student for a 6-month research position supporting an AI-enhanced coastal resilience project focused on compound flood and extreme heat hazards in Hampton Roads. The student will play a central technical role in collecting, preprocessing, and spatially standardizing multi-source geospatial datasets (including flood depth raster, WRF heat outputs, Nature-based Solutions layers, and socio-environmental variables). Responsibilities include building modeling-ready spatial databases, constructing spatial adjacency matrices, implementing a Spatial Graph Neural Network (GNN) to generate a Compound Flood–Heat Index (CFHI), conducting spatial machine learning analyses with SHAP-based explainable AI, and assisting with Reinforcement Learning simulations to optimize Nature-based Solutions allocation.
The position requires strong Python programming skills, experience with geospatial data processing (e.g., GeoPandas, Rasterio), familiarity with machine learning methods, and comfort working with large raster datasets. Experience with PyTorch (or similar frameworks), spatial statistics, or reinforcement learning is highly desirable. This role offers hands-on experience in advanced spatial AI, interdisciplinary collaboration, and opportunities for co-authorship in peer-reviewed publications.

Interested students should send a CV and a one-page statement of interest describing relevant skills, coursework, and research experience to Dr. Sina Razzaghi Asl, srazzagh@odu.edu