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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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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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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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common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel programming with one or more common parallel programming models, like MPI
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. Experience with heterogeneous and parallel computing technologies, programming models, or accelerators, including CPUs, GPUs, FPGAs, and emerging computing technologies. Familiarity with quantum software
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Language Models (LLMs). Distributed Machine Learning: Specialization in data parallelism, model-parallelism, and collective communication strategies in large-scale environments. Proficiency in frameworks
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well as experience with HPC environments and parallel computing. Demonstrated hands-on experience and understanding of developing scientific data management, workflows and resource management problems. Strong problem
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massively parallel algorithms and code performance profiling are a plus. Special Requirements: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must
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or model parallel training. Experience with multi-physics simulations on HPC and with ML models. Experience working in a multi-disciplinary research environment. Demonstrated written and oral
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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