36 parallel-programming-"Fields-Institute" Postdoctoral positions at Oak Ridge National Laboratory
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management, intermediate representations, or execution orchestration for quantum or heterogeneous computing platforms. Experience with heterogeneous and parallel computing technologies, programming
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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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discovery. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially
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position. Experience with neutron transport and reactor analysis software (SCALE code system, MCNP, OpenMC, Serpent, VERA, MOOSE codes, etc.). Experience with common programming and scripting languages
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geothermal casing related harsh environments applications. A background in polymer chemistry research or related fields, composite material development, material science, and data analysis is preferred
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expertise in multiscale and multifidelity simulation techniques ( ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or
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foundation in linear algebra (tensor networks and beyond) and quantum physics Familiar with programming languages and scientific computing Preferred Qualifications: Experience working with low-rank
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professional society conferences and workshops. Preferred Qualifications: Experience with probabilistic programming frameworks such as PyMC, Stan, NumPyro, or similar. Experience with system
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: Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or a closely related field. Strong programming proficiency in C, C++, and Python. Hands-on experience with embedded systems
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numerical methods, high-performance computing (HPC), or large-scale data analysis. Experience in applying AI/ML techniques to hydrological and Earth sciences. Proficiency in scientific programming