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extracting transport properties from molecular dynamics trajectories. ● Experience with GPU-accelerated machine learning frameworks (for example CUDA, PyTorch, or GPU-enabled LAMMPS). ● Experience
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skills. Other qualifications: Documented experience in the development and optimization of scientific software for GPU-based computing platforms. Experience with programming using CUDA, HIP, SYCL, OpenACC
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frameworks such as Qiskit, CUDA-Q, PennyLane, Cirq, or equivalent platforms, and knowledge of quantum algorithms, quantum error correction, fault-tolerant computing, or quantum resource estimation. Experience
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application programming models such as CUDA, HIP, SYCL, OpenMP, Kokkos or Raja, vectorization; MPI; and one-sided asynchronous programming models. Experience in heterogeneous computing, developing and debugging
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distributed codes using MPI, OpenMP, CUDA, ROCm, and related HPC technologies while bridging theoretical AI models with real hardware constraints. Cross‑Paradigm Integration(new optional emphasis): Explore how
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system diagnostics. Experience with CUDA and deployment on NVIDIA edge-GPU platforms (Jetson Orin, Thor, or Spark). Experience calibrating and synchronizing LiDAR, IMU, camera, and wheel-odometry systems
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will be viewed highly favorably. Experience with GPU acceleration or code parallelization paradigms (e.g., MPI, OpenMP, CUDA). FLSA Exempt Full Time/Part Time Full Time Number of Hours Worked per Week 40
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reduction, nonlocal PDE operators or OpenMP/MPI/CUDA parallelization? If such terms are familiar for you - and if you are excited by the mathematical analysis, development, and rigorous understanding
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their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
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information science research. *Experience with theoretical frameworks for quantum entanglement and decoherence. *Desired, though not mandatory, expertise in quantum platform software such as Qiskit or cuda-q