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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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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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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
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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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avoidance, autonomous exploration, frontier selection, and localization-aware trajectory planning. Develop high accuracy point cloud registration and mapping workflows. Plan and conduct experiments