Team Leader
Benjamin Borja
Texas A&M University
Biomedical Sciences
benborja@tamu.edu
Project Type
Research
Who Can Join
Graduate Students, Masters Students, Undergraduate Students
Project Description
This project conducts research exploring the simulation of computational fluid dynamics, the collection and integration of datasets, and the creation of machine learning models. Students will help address practical challenges such as data quality, feature engineering, and uncertainty handling while also experimenting with tools and workflows for scalable data storage and experiment tracking. This is a unique opportunity to work at the intersection of CFD, data engineering, and machine learning.
Team Needs
We are looking for students who are creative, reliable, self-driven, and comfortable working both independently and collaboratively. While no prior research experience is required, team members must be motivated and detail-oriented learners willing to dedicate time and hard work to practicing the associated skills required to contribute to the project. We are looking for students with a background in Computational Fluid Dynamics (including fluid mechanics, turbulence modeling, numerical methods, OpenFOAM proficiency, and mesh generation), atmospheric sciences (including boundary layer meteorology, atmospheric stability, WRF familiarity, and flow regimes), programming (such as Python, C++, HPC/MPI, and Git), and data handling (such as HDF5/NetCDF, dataset integration, statistical literacy, and uncertainty quantification).
Special Opportunities
– Exposure to in-demand topics and skills, including machine learning, computational fluid dynamics workflows, and data science
– Hands-on experience in contributing to a product
– Develop skills in analyzing and contributing to research
– Learn how to work effectively as a member of a research team to meet deadlines and achieve goals
– Flexible schedule: build your own schedule around our one meeting a week
– Exposure to CFD, ML, and data organization programs