Large-spin models have recently attracted significant interest following breakthroughs in cold atom experiments. However, modeling large-spin systems poses substantial challenges due to their high dimensionality and intricate symmetries, making standard numerical methods inadequate for accessing certain experimental regimes. A novel approach proposed by one of the project supervisors, circumvents the need for Clebsch-Gordan coefficient calculations, offering a promising solution to the limitations of traditional methods. This internship will build upon this approach in several stages. First, the student will explore group theory, focusing on SU(N) representations, which are essential for understanding large-spin systems. A strong theoretical foundation in these concepts is crucial for the project's later stages. Next, the student will implement an exact diagonalization method to simulate quantum models with high accuracy. Various models, including Heisenberg chains and ladders, will be considered, where analytical techniques often fall short. Finally, with a view toward PhD-level research, the student will explore tensor network methods. These are powerful tools for simulating low-dimensional quantum systems, particularly spin chains, and offer unprecedented precision in capturing key properties. The objective is to adapt existing tensor network algorithms to incorporate this new computational approach, further advancing the study of large-spin systems.