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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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areas include: Parallel and distributed graph and or ML algorithms — shortest paths, connectivity, clustering, or graph traversal on GPU clusters and distributed-memory systems. Sparse direct and
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leadership-class computing systems and institutional clusters. Write robust Linux bash scripts and job submission scripts for SLURM and PBS environments, including multi-node GPU/CPU workflows, monitoring
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execution environments on both CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure
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repositories, GPU computing. Development of agentic workflows for scientific applications. AI methods for CFD and FEM; multiphysics code integration. Experience with open-source fusion engineering codes (FESTIM
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programming with one or more common parallel programming models, like MPI or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively
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stack, lifecycle-managed nodes, TF2, rosbag workflows, and distributed system diagnostics. Experience with CUDA and deployment on NVIDIA edge-GPU platforms (Jetson Orin, Thor, or Spark). Experience