Post Doctoral Researcher, Department of Geography & Spatial Sciences

Project Summary

This project, Digital Twins of Coastal Ecosystems, aims to investigate the effects of salinity changes caused by flooding and sea-level rise (SLR) on the chemical, biological, physical and engineering properties of coastal soils. The University of Delaware (UD) team will examine how these evolving soil properties affect biogeochemical processes and functioning of the soils, and the ecosystem services they provide. To support this work, we will develop advanced modeling capabilities to quantify and predict the responses of coastal ecosystems to disturbances caused by flooding and SLR. Ultimately, this effort will inform the development of strategies to mitigate the impacts of flooding and SLR on both natural coastal ecosystems and the built environment (e.g., facilities and installations).

Focus Area : Water Quality, Hydrological and Hydrodynamic Modeling

Background

Coastal regions are increasingly threatened by sea-level rise (SLR), flooding, and saltwater intrusion, which disrupt soil stability, water quality, and ecosystem services. These disturbances alter hydrological and biogeochemical processes, affecting carbon and nitrogen cycling, soil health, and biodiversity. To address these challenges, the University of Delaware has undertaken extensive field data collection, including real-time monitoring of the physical and biochemical properties of the subsurface environment at the study site. This data underpins the development of advanced water quality, hydrologic, and hydrodynamic models that can simulate the complex interactions between coastal terrain and the subsurface environment under changing climate conditions. By integrating field data with predictive modeling, this project aims to quantify the impacts of salinity changes on water and soil systems, providing critical insights into how coastal ecosystems respond to disturbances caused by SLR and flooding.

Responsibility

• Develop and implement hydrodynamic and hydrologic models to assess soil and water properties and ecosystem responses to environmental stressors, such as sea-level rise (SLR) and flooding.
• Integrate field data (e.g., salinity, nutrient levels, soil and water properties) into the development of numerical models to enhance predictive accuracy.
• Apply machine learning and data-driven approaches to improve model performance and interpretability, incorporating uncertainty quantification across a range of scenarios.
• Other responsibilities include publishing findings on peer-reviewed journals, presenting progress at professional meetings, preparing project reports and deliverables, and collaborating with other research teams.

Qualifications

Preferred Skills :

To Apply :

Any questions can be directed to Dr. Yao Hu (yaohu@udel.edu). Applicants should complete an online application and include a cover letter, CV (including a list of publications), and the names and contact information of three references. Incomplete applications will not be reviewed. Qualified applicants will be reviewed immediately upon receipt of the application, and the search may continue until the position is filled.

Salary is competitive and commensurate with your experience in relevant fields, along with a comprehensive benefits package. The position is for two years, with the possibility of renewal for additional years, contingent upon satisfactory performance and funding availability.

General information about UD

The University of Delaware is a tier-1 research university and ranks among the top 100 universities in federal R&D support for science and engineering. It provides an excellent research base for interdisciplinary work with its 194,000-square-foot Interdisciplinary Science and Engineering Laboratory (www1.udel.edu / iselab). Newark, Delaware, is a vibrant college town on the mid-Atlantic coast of the U.S., located within two hours of Philadelphia, Baltimore, Washington D.C., and New York City. The University of Delaware is an