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, or network-based modeling of infrastructure or industrial systems. Familiarity with high-performance computing, cloud computing, or parallel computing environments for training models and solving optimization
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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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(HPC): Experience with parallel computing (MPI, OpenMP, CUDA/HIP) or running workflows on supercomputing clusters. Software Engineering: Knowledge of version control (Git), containerization (Docker
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as
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chemistry and experience with quantum chemistry packages (e.g., Molpro, NWChem) Strong skills in developing and implementing computational and numerical methods; familiarity with parallel computing on CPU/GPU