Our HACLab @ Baylor is seeking 2 PhD positions (Fall ’26) in spatiotemporal ML for climate–health. Priority Dec 1, 2025. Apply ➜ https://haclab.io/phd2026 #PhDposition #ClimateHealth #GIS #HPC #Baylor
PhD Positions (2) – Health, Agriculture & Climate Lab (HACLab), Baylor University
Start: Fall 2026 • Location: Waco, Texas • Advisor: Dr. Erich Seamon
The Health, Agriculture & Climate Lab (HACLab) at Baylor University invites applications for two funded PhD positions beginning Fall 2026. We develop methods and tools at the intersection of spatiotemporal modeling, machine learning, and health & climate analytics, with opportunities to prototype immersive VR/XR decision-support experiences. Our work spans large-scale environmental and health datasets, reproducible pipelines in Python and R, and building ML/statistical models that can bridge natural systems with human systems. We have a particular need for students who want to work with: CDC BRFSS health data; Yale’s climate communications survey data; downscaled climate GRIDMET data; and agricultural yield and/or insurance data. However, we encourage students to apply who have unique interests that may be not be directly associated with these areas.
Research focus
- Spatiotemporal ML for climate–health risk prediction (county to census-tract scale)
- Model fusion and explainability (RF/GWRF, gradient boosting, deep nets, SHAP, local MDA)
- Climate reanalysis & remote-sensing integration (e.g., netCDF/HDF5, ERA5, TerraClimate, GRIDMET, PRISM)
- Interactive visualization & VR/XR for stakeholder engagement and scenario exploration
Responsibilities
- Design and implement analyses in Python (e.g., xarray/dask, rasterio, geopandas) and R (sf, terra, tidymodels/caret)
- Build scalable workflows on HPC (e.g., SLURM, containers, GPU jobs)
- Work on collaborative research teams that connect climate, health, or agricultural data with ML processes
- Manage large spatiotemporal datasets (netCDF, cloud-optimized rasters), ensure reproducibility (Git/GitHub)
- Prepare manuscripts, conference papers, and open/reusable code & data products
- Collaborate with multi-institutional teams and mentor undergraduate RAs; optional contributions to VR/XR prototypes (Unity/Unreal/WebXR)
Minimum qualifications
- Bachelor’s or Master’s in geography/GIS, environmental science, data science, CS, statistics, public health, or related field
- Strong programming in Python and/or R, or a willingness to become more proficient!
- Coursework or experience in statistics/ML and spatial analysis/GIS
- Evidence of careful, reproducible research (e.g., GitHub portfolio, well-documented code)
Preferred qualifications
- Experience with xarray/dask, netCDF/HDF5, GDAL, raster processing, or geodatabases
- HPC experience (Linux, SLURM, job arrays, containerization via Docker/Apptainer) and/or GPU workflows
- Geostatistics (kriging, variograms), spatial econometrics, causal inference, or epidemiology
- Remote sensing (Sentinel/Landsat, atmospheric correction)
- VR/XR development (Unity/Unreal, WebXR), or 3D data viz
- Peer-reviewed publications or substantial preprints
How to apply
Email a single PDF titled Lastname_Firstname_HACLab2026.pdf to info@haclab.io with:
- Cover letter (1–2 pages) describing your fit and which research directions excite you
- CV (with links to code/data projects)
- One writing sample or first-authored code repository
- Make the subject of the email: HACLAB 2026 PHD APPLICANT. If you do not do this, the email will not be routed to the right location.
Priority deadline: December 1, 2025 (rolling review thereafter until filled).
Applicants should also submit a formal application to the appropriate Baylor PhD program after initial informal review. PLEASE MAKE SURE TO CONTACT US THRU THE ABOVE STEPS BEFORE FORMALLY APPLYING TO BAYLOR.
About HACLab @ Baylor
HACLab advances scalable, decision-relevant models at the interface of climate, health, and agriculture, emphasizing transparent methods, high-quality data engineering, and actionable visualization. Learn more at HACLab https://haclab.io
Equal Opportunity: Baylor University is an Equal Opportunity employer. All qualified applicants will receive consideration without regard to race, color, national origin, sex, age, disability, veteran status, or any other protected status.