Part-Time Research Assistant
About The Position
The College of Earth and Mineral Sciences, John and Willie Leone Family Department of Energy and Mineral Engineering is seeking applicants for a part-time job of Research Assistant. The project involves developing a data-driven, physics-informed modeling framework to improve the predictive capability of population balance models (PBMs) for stirred media milling. This research will integrate DEM-CFD simulation data to resolve the underlying microscale mechanisms governing particle breakage. A physics-informed graph neural network (PI-GNN) framework will be constructed, where the mill domain is represented as a spatial graph. The PI-GNN will be trained to learn spatially distributed PBM closure terms using local physical descriptors obtained from DEM-CFD simulations. These learned closures will then be embedded into a PBM solver to predict particle size distribution evolution and to identify high-energy breakage regions within the mill. The overall objective is to establish a multiscale modeling framework that links particle-scale physics with macroscale milling performance, enabling more accurate and physically grounded predictions of grinding efficiency. This work will serve as a proof-of-concept for incorporating machine learning–based closures into mechanistic comminution models.
Requirements
- Data-driven, physics-informed modeling framework development
- Population balance models (PBMs) for stirred media milling
- DEM-CFD simulation data integration
- Physics-informed graph neural network (PI-GNN) construction
- Training PI-GNNs using local physical descriptors
- Embedding learned closures into PBM solvers
- Multiscale modeling framework establishment
- Machine learning-based closures for comminution models
Responsibilities
- Develop a data-driven, physics-informed modeling framework to improve the predictive capability of population balance models (PBMs) for stirred media milling.
- Integrate DEM-CFD simulation data to resolve the underlying microscale mechanisms governing particle breakage.
- Construct a physics-informed graph neural network (PI-GNN) framework.
- Train the PI-GNN to learn spatially distributed PBM closure terms using local physical descriptors obtained from DEM-CFD simulations.
- Embed learned closures into a PBM solver to predict particle size distribution evolution and to identify high-energy breakage regions within the mill.
- Establish a multiscale modeling framework that links particle-scale physics with macroscale milling performance.
Benefits
- Compensation: The starting rate for this job is $41.19.
- Employment with the University will require successful completion of background check(s) in accordance with University policies.
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What This Job Offers
Job Type
Part-time
Career Level
Entry Level
Education Level
No Education Listed
