Neutrinoless double beta decay (0nßß) represents a pivotal area of research in nuclear physics, offering profound insights into neutrino properties and the potential violation of lepton number conservation. The CUPID experiment is at the forefront of this investigation, employing advanced scintillating bolometers at cryogenic temperatures to minimize radioactive background noise. It aims to achieve unprecedented sensitivity in detecting 0nßß decay using lithium molybdate (Li2MoO4) crystals. These crystals are particularly advantageous due to their scintillation properties and the high Q-value of the decay process, which lies above most environmental gamma backgrounds. In turn this endeavour will require operating a fine grained array of 1596 dual heat/light detectors with excellent energy resolution. The proposed thesis integrates artificial intelligence (AI) techniques to enhance data analysis, reconstruction, and modeling for the CUPID experiment demonstrators and the science exploitation of CUPID.

The thesis will focus on two primary objectives:
1. Improved Time Series Event Reconstruction Techniques
- CNN based denoising and comparison against optimal classical techniques
2. Multivariate science analysis of a large neutrino detector array
- Analysis of Excited States: The study will use Geant4 simulations together with the CUPID background model as training data to optimize the event classification and hence science potential for the analysis of 2nßß decay to excited states.