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computing software libraries (e.g., Trilinos, MFEM, PETSc, MOOSE). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance
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simulated and measured results to assess quantities of interest. Interface with world-class exascale computing clusters. Work with a dynamic team of researchers, developers, experimentalists, and model
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computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other
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functional genomics, human genetics, and in vivo experimental systems to understand enhancer function across regulatory and phenotypic scales. We develop and apply both experimental and computational
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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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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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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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/O solutions (e.g., HDF5, ADIOS2), and distributed computing tools relevant to data preparation. Evidence of ability to conduct independent research and publish in peer-reviewed venues. Preferred
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software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
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, Mixture-of-Experts; distributed training/inference (e.g. FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation pipelines for reasoning and agents. Federated & Collaborative