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, including pretrained universal models and models tailored to specific chemical and materials systems. Develop, debug, profile, and optimize scientific codes and workflows on massively parallel computing
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understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred Qualifications: Experience in implementing ab initio simulation codes such as VASP
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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laser arrays Optimization and acceleration of existing simulation codes Parallel computing and high-performance computing (HPC) GPU acceleration, where appropriate Development of scalable numerical
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(e.g. weather, hydrology). 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
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the following areas is preferred, and an exceptionally strong candidate in a single area is also encouraged to apply. Relevant areas include: Parallel and distributed graph and or ML algorithms
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to candidates having a strong record in developing novel computational strategies to solve complex physics problems with relevant experience in nonlinear analysis of pdes and parallel computation. FLSA Exempt
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architectural design decisions for massively parallel heterogeneous systems, including pathways that integrate quantum or analog co-processors. Performance Engineering at Scale: Profile, model, and optimize
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of finite element simulations methods. Experience using parallel Linux computing platforms, parallel job submission scripts, common software repository tools (e.g., GitHub), and parallel visualization
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, highly collaborative environment. Demonstrated ability to carry out a variety of tasks in parallel, recognize and solve problems effectively, and shift priorities rapidly. Selected Publications Intra- and