  In this contribution we present a status report on the recent progress
towards an analysis of nuclear parton distribution functions (nPDFs) using the
NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to
account for the dependence on the atomic mass number $A$, and introduce novel
algorithms to improve the training of the neural network parameters within the
NNPDF framework. Finally, we present preliminary results of a nPDF fit to
neutral current deep-inelastic lepton-nucleus scattering data, and demonstrate
how one can validate the new fitting methodology by means of closure tests.
