RANS simulation are one of the most used tools for aerodynamic analysis. The advantage of RANS simulations is the reduced computational cost compared to other methods such as LES or DNS. This is because by solving the RANS equations one only solves for the mean flow. However, this reduction in computational cost comes at the price of uncertainty. During the derivation of the RANS equations a closure problem is introduced. The turbulence models that solve this closure model of the Reynolds stress introduce the largest level of uncertainty. In this research symbolic regression is used to augment the k-ω-SST turbulence models with corrections directly inferred from high fidelity data sources such as DES, LES or DNS. This model is augmented with a correction to the anisotropic part of the Reynolds stress and a direct correction to the turbulence production. These correction fields are calculated through the so called frozen approach, where the k- and ω-transport equations are solved using the frozen mean flow values from the high fidelity data. Symbolic regression with Pope's eddy viscosity hypothesis as a basis is used to find models for these correction fields. With these correction models the k-ω-SST is extended to a non-linear turbulence model for the prediction of the eddy viscosity. Elastic net regression forms the basis of the machine learning method and is used to promote sparsity of the correction models. The sparsity is needed for stability and to keep the computational efficiency of the RANS method. This symbolic regression method is named SpaRTA and has been successfully applied to 2D cases in literature. In this work that is extended to 3 fully 3D cases, namely a wall mounted cube, an infinite circular cylinder and an idealised rotating wheel on a road surface. This study has shown the applicability of the SpaRTA methodology to these cases, resulting in models that have the extrapolating capabilities to improve the results on all 3 test geometries.