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Post Doc Res Assoc Job Summary The Ho lab at the University of Utah is launching an ambitious program to systematically discover, map, and therapeutically harness mitochondrial microproteins
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foundations of economic decision-making, using behavioral experiments and computational modeling. The program is especially interested in research that explores parallels between the cognitive mechanisms
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workloads. Conduct research on language front‑end abstractions, mixed‑precision modeling, heterogeneous parallelism, and MLIR-level transformations. HPC System Co‑Design: Work with domain scientists and
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adapting large-scale codes developed for parallel computing. Record of excellence in professional achievement, as evidenced by a strong publication record. Demonstrated ability to carry out independent
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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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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environments. Experience with parallel computing environments, HPC in a Linux environment. Experience with surrogate modeling. Experience with data analytics techniques. Familiarity with C++ and GPU programming
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also include trouble shooting capabilities. Desirable skills include working knowledge of Linux, FORTRAN, Python and C++; experience with machine learning, parallel computing and high-performance
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, turbulence, CFD, numerical methods, and turbulent combustion. • Proficiency in HPC environments (parallel computing on CPU/GPU systems) • Demonstrated publication record in reputable journals/conferences
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
Job Description We are seeking an interdisciplinary postdoctoral researcher to join our lab at St. Jude Children's Research Hospital to develop new experimental-computational approaches for studying