The DOE CSGF's math/computer science track is intended for doctoral candidates in applied mathematics, statistics or computer science who undertake research that will contribute to more effective use of emerging high-performance computer systems.
Whereas traditional track applicants must have a specific science or engineering application for their research, math/computer science track candidates are expected to focus on fundamental research into enabling technologies that are broadly relevant to science and engineering applications of interest to DOE.
Such areas include (but are not limited to):
- ODE, PDE, and integral discretization methods
- Linear and nonlinear solvers
- Multiscale, multi-physics coupling methods
- Verification, validation, and uncertainty quantification
- In situ data analysis
- High-dimensional data analysis
- Large-scale data visualization
- High-performance compilers
- Programming models and abstractions for heterogeneous computing
- Domain-specific languages
- Dynamic runtime environments
- Power management
- HPC development tools
- HPC performance analysis and tools
- Debugging at extreme scale
- Scalable I/O
- Scalable machine learning
- Interpretable machine learning
- Physics-constrained machine learning
- Robust machine learning
- Scientific data management and engineering
The interdisciplinary program of study for fellows in this track will still include science and engineering course requirements, ensuring that they are exposed to the computational needs of applications that will use these new enabling technologies.
Please contact us with questions about this track.