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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using
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materials technologies. We focus on the unique physical properties and disruptive application potential of two-dimensional materials, leverage intelligent materials design and multi-scale computational
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to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multi-purpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic
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-Based Generative Models: How can we fundamentally redesign generation processes for superior efficiency, controllability, and quality? We are exploring diffusion models, flow-matching, and other parallel