Population-based finite element analysis of hip joint models enables us to study the effect of inter-subject variability on the simulation results. This approach is the dawn of computerized clinical trials and offers various contributions to improving current clinical treatment, the design of surgical simulators for virtual training, and optimizing hip implants. Despite the broad application of population-based analyses, developing large-scale subject-specific models is a challenging task and requires extensive manual effort. Thus, most state-of-the-art studies are limited in the number of subjects, and the anatomical representations are often subjected to simplifications. For instance, the geometry of the hip joint area is replaced with simple shapes, or the bilateral variation in this area is ignored. Such assumptions may limit the reliability of the predicted results. This thesis provides novel methods for developing finite element models that enable large-scale population-based assessment of hip joint behavior. We benefit from the power of computer science to improve the quality and automate the standard modeling approaches. We further employ our new pipeline to generate multiple subject-specific finite element models, including the bones and cartilages in the hip joint area. The subject-specific finite element models are clinically validated and have high-quality discretization with accurate geometries. These subject-specific models demonstrate different mechanical behavior across subjects within the same simulation scenario. Additionally, the simulation results vary between the left-hand and right-hand side of the body in each subject. Our work is one of the largest model repositories concerning the number of subjects and regions of interest. We aim to empower researchers with free access to verified and reproducible computational models. Thus, our detailed research data, including the clinical images, the segmentation label maps, the finite element models, and software tools, are openly accessible on https://diku-dk.github.io/libhip/. In future work, we aim to add additional structures to our models and upscale the population size as a direct benefit of our approach.