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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering
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on theoretical and computational aspects of quantum many-body systems, including Tensor Networks, Neural Quantum States, Stabilizer formalism, Complexity measures such as entanglement and quantum magic, quantum
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learning libraries (such as TensorFlow, PyTorch), along with experience in structural modeling tools (e.g., Vienna, Rosetta, RNAstructure) and graph neural networks or transformers applied to molecular
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