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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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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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topics in quantum error correction. The position includes close interaction with experimental collaborators in Trieste. The ideal candidate should have a strong background in condensed matter theory
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swarm optimisation, 4) critical phenomena in field theory, 5) and the Schwinger-Keldysh formalism of hydrodynamics. About the group: The STELLAR lab is headed by three staff members, Andrea Amoretti