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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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pipelines). Familiarity with high-performance computing (GPU, CUDA, Slurm, etc.). Notions of full-stack software development or a willingness to develop these skills. You possess strong problem-solving and
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dynamical systems High-performance computing Parallel numerical algorithms GPU computing Strong programming skills are very important. Experience with C/C++, Python, Julia, MATLAB, CUDA, or a comparable
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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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on modern accelerators. Sparse and constrained tensor decompositions — CP- or Tucker-type factorizations, constrained least-squares solvers, or scalable tensor kernels. GPU performance engineering — CUDA/HIP
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, scalability, and efficient utilization of computational resources. Experience with GPU accelerators and computing ecosystems such as CUDA and/or AMD ROCm/HIP. Experience with performance profiling and
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& Training, Network Programming (UDP/TCP/ZeroMQ), API Development, CUDA programming, python programming, Hardware Acceleration, strong verbal and written communication skills necessary for writing research
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to the development of the HashBrick library, an open source framework for adaptive mesh refinement, written in CUDA and supporting one-sided communication for scalability. The target architecture is a supercomputer
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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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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