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Field
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with GPU-based code development (e.g., CUDA). - Familiarity with AI/ML development concepts such as RL, RAG, MCP, Agentic AI, multi-modal learning, etc. - Familiarity with AI/ML development tools such as
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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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, deployment, and test activities • Experience integrating software with hardware, sensors, or embedded systems • Familiarity with GPU-based code development (e.g., CUDA, TensorFlow, etc.) • Familiarity with AI
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RFSoC platforms, and CUDA coding for GPU acceleration.
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simulators. CUDA. C++. Python. Other requirements to consider: Motivation and organizational skills. Analytical capacity and rigor. Multidisciplinary fit into an international research group. Specific
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. Experience with hybrid quantum-classical workflows. Proficiency with Qiskit, PennyLane, CUDA-Q or equivalent. Strong programming skills and familiarity with AI/ML tools. Experience in industry collaborations
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Qualifications: Competency in C, Rust or Julia. Experience in software design/architecture Experience with parallel programming middleware, including MPI, OpenMP and CUDA. Knowledge of research software
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software including Red Hat Linux, CentOS, OpenStack, NVIDIA CUDA, TensorFlow, SQL Server, and MySQL. A large-capacity Ceph storage system is available for standard and archival storage. CAC configured and
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modules, AWS, Azure, GCP, or comparable services. Experience supporting GPU, AI/ML, CPU-intensive, statistical, imaging, compiler/toolchain, Fortran, MATLAB, R, Python, CUDA, or other discipline-specific
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of Alps, managing the full dependency stack (CUDA, NCCL, PyTorch, training and serving frameworks) Keep image builds reproducible, versioned, and documented, including CI for builds and upgrades Validate