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of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a significant computational component in deploying multi-GPU codes
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Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or JAX Experience with HPC and GPU-accelerated computing Familiarity with foundation models / LLMs; interest in reproducible
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, optimized for high-performance computing (HPC) environments Classifying ice crystal habits using Convolutional Neural Networks (CNNs) Providing intuitive graphical interfaces for user interaction and data
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megawatts. To transfer energy efficiently from the grid to CPUs/GPUs, higher system voltages are required in data centres/computer racks, and efficient power electronics converter systems based on SSTs