STEM / applied
Computational physics · Graduate · Physics
Topics
High-performance computing
- MPI domain decomposition
- OpenMP and hybrid parallelism
- GPU kernels for stencil and FFT workloads
- I/O at scale: HDF5 and parallel filesystems
- Job scheduling on clusters and clouds
Machine learning interfaces
- Neural network potentials for MD
- Surrogate models for expensive solvers
- Physics-informed neural networks cautions
- Automated experiment design with BO
- Ethics of ML in scientific inference
Research software
- Contributing to open-source physics codes
- Software citation and credit norms
- Pair programming in theory–computation teams
- Performance profiling with vendor tools
- Publishing simulation supplements
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.