  Perturbative quantum chromodynamics (QCD) ceases to be applicable at low
interaction energies due to the rapid increase of the strong coupling. In that
limit, the non-perturbative regime determines the properties of quarks and
gluons (partons) in terms of parton distribution functions (PDFs) or nuclear
PDFs, based on whether they are confined within nucleons or nuclei
respectively. Related non-perturbative dynamics describe the hadronisation of
partons into hadrons and are encoded by the fragmentation functions (FFs). This
thesis focuses on the detailed study of PDFs in protons and nuclei as well as
the charged pions FFs by means of a statistical framework based on machine
learning algorithms. The key ingredients are the Monte Carlo method for error
propagation as well as artificial neural networks that act as universal
unbiased interpolators. The main topics addressed are the inference of proton
PDFs with theoretical uncertainties and the impact on the gluon PDF from dijet
cross-sections; a global determination of nuclear PDFs exploiting the
constraints from proton-lead collisions at the LHC and using for the first time
NNLO calculations; a new determination of FFs from single-inclusive
annihilation and semi-inclusive deep-inelastic scattering data; and a
quantitative assessment of the impact of future colliders such as the
High-Luminosity LHC and the Electron-Ion Collider on the proton and nuclear
PDFs.
