- We are recruiting a fully funded Ph.D. student (tuition+stipend) starting in the Spring or Fall of 2024.
- We are looking for a fully funded Postdoctoral Scientist to join our lab. The ideal candidate is expected to have a strong background and interest in applying AI techniques to Ecological and Earth system research. The position is open until filled.
Check out the information below for more details.
We are looking for self-motivated undergraduate and graduate students as well as postdoctoral researchers to join our lab and work together to promote our understanding of the complex Earth system dynamics. Feel free to contact Prof. Min Chen for opportunities.
There are always opportunities to motivated undergraduate students who have a strong interest in Earth system science. Prospective students are expected to have strong computer programming skills, math and physics backgrounds. Funding support may be available depending on project needs.
Our group is particularly interested in questions regarding the interactions between climate change, terrestrial ecosystems, and human society. We develop and use Earth system models (both process-based and empirical), various remote sensing and field data, integrated assessment models, and model-data fusion as the major research tools to answer our research questions.
We are looking for talented and motivated students who are expected to work on one or more of the following general topics:
- Modeling the land-atmosphere exchanges of carbon, water, and energy across different spatial and temporal scales.
- Integrating Artificial Intelligence tools with Earth system research
- Using remote sensing data for understanding how ecosystem carbon/water/energy cycle responds to environmental changes.
- Vegetation radiative transfer modeling and its applications in understanding ecosystem processes in combination with Earth system models and remote sensing.
- Linking the natural Earth system model with integrated assessment models (e.g., GCAM) to understand human-Earth system interactions that infer policymaking.
All applicants should meet the minimum requirements by graduate admission (https://grad.wisc.edu/apply/). International students should also meet the minimum requirement of TOEFL or IELTS. We are particularly interested in students who are comfortable with computer programming and have strong quantitative skills.
Prospective students are encouraged to contact Prof. Min Chen (email@example.com) to discuss potential research projects and opportunities before their applications. Please include your CV, transcripts, names, and the contact information of up to three references, and a personal statement that describes your research interest, experiences, and skills relevant to the lab’s research directions. CV is a must and the others are optional but encouraged. We greatly appreciate all the applications, but we will only give feedback to the candidates that we plan to interview.
The University of Wisconsin-Madison is a world-renowned academic institution and is ranked as a top public research university. The Department of Forest and Wildlife Ecology is a campus hub for applied ecology, spatial analysis and remote sensing of ecosystems, and the social sciences related to natural resource management and conservation. Our Forest Science program was ranked #1 in the nation in 2020 by the National Research Council. The student will also have the opportunity to collaborate with scholars at other departments and prestigious institutions in the US and the world.
Situated on the isthmus between Lakes Monona and Mendota, UW-Madison, and the Madison area are renowned for safety, vibrant culture, scenic beauty, an abundance of recreational and entertainment opportunities, exceptional K-12 schools, a friendly feel, and a high quality of life. Madison was ranked #1 Best Place to Live by livability.com.
We usually look for postdoctoral scientists to join us to work on scientific and technical problems in the field of Earth system science, especially related to land-atmosphere interactions. We are particularly interested in those who have a strong background in data analysis, land surface modeling, integrated assessment modeling/climate change economics, data assimilation (including machine learning), vegetation radiative transfer modeling, or remote sensing applications.
Interested ones are also encouraged to contact Dr. Min Chen for collaboration on potential fellowship applications (e.g., NSF and NOAA postdoctoral fellowship) or proposal ideas.