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the Computer Science program at New York University Abu Dhabi, seeks to recruit a research assistant to work on the intersection of compilers and deep learning. Many companies, such as Google, Facebook, and Amazon
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning
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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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willingness to learn: High-performance computing (distributed systems, profiling, performance optimization), Training large AI models (PyTorch/JAX/TensorFlow, parallelization, mixed precision), Data analysis
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, AMD uProf, or Omniperf. Debugging experience with distributed-memory parallel applications. Experience with containers (Docker, Podman, Shifter or similar) and modern software practices such as Git
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Deadline 7 Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by
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Mathematics, or a related field, awarded within the last five years Programming experience in one or more of Python, C++, Fortran, or Julia Knowledge of high-performance and parallel computing Experience
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implementing, optimizing, or integrating quantum libraries such as Itensor, CUDA-Q, Qiskit, or PennyLane. Experience debugging and profiling distributed-memory parallel applications. Knowledge of Git and modern
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. Experience implementing, optimizing, or integrating quantum libraries such as Itensor, CUDA-Q, Qiskit, or PennyLane. Experience debugging and profiling distributed-memory parallel applications. Knowledge
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for an accurate simulation of time-dependent flows, enabling sensitive applications such as aeroacoustics. Furthermore, the high scalability on massively parallel computers can lead to advantageous turn-around