  We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning
model that predicts various nuclear observables, including binding and decay
energies, and nuclear charge radii. The model is trained using a multi-task
approach with shared representations and obtains state-of-the-art performance,
achieving levels of precision that are crucial for understanding fundamental
phenomena in nuclear (astro)physics. We also report an intriguing finding that
the learned representation of NuCLR exhibits the prominent emergence of crucial
aspects of the nuclear shell model, namely the shell structure, including the
well-known magic numbers, and the Pauli Exclusion Principle. This suggests that
the model is capable of capturing the underlying physical principles and that
our approach has the potential to offer valuable insights into nuclear theory.
