KM3NeT/ORCA is a large-volume water-Cherenkov neutrino detector, currently under construction at the bottom of the Mediterranean Sea at a depth of 2450 meters. The main research goal of
ORCA is the measurement of the neutrino mass ordering and the atmospheric neutrino oscillation
parameters. Additionally, the detector is also sensitive to a wide variety of phenomena including
non-standard neutrino interactions, sterile neutrinos, and neutrino decay.
This contribution describes the use of a machine learning framework for building Deep Neural
Networks (DNN) which combine multiple energy estimates to generate a more precise reconstructed neutrino energy. The model is optimized to improve the oscillation analysis based on
a data sample of 433 kton-years of KM3NeT/ORCA with 6 detection units. The performance
of the model is evaluated by determining the sensitivity to oscillation parameters in comparison
with the standard energy reconstruction method of maximizing a likelihood function. The results
show that the DNN is able to provide a better energy estimate with lower bias in the context of
oscillation analyses.