ABSTRACT We aim to improve X-ray spectroscopy with an advanced detector that captures a unique signal for each photon. This detector measures a distinct signal for each single photon. This signal needs to be modeled efficiently and fast. Traditional pulse processing techniques struggle to efficiently and accurately model these signals, especially when dealing with up to 1e14 signals necessary for extracting comprehensive spectra. Our project seeks to leverage transformer models, celebrated for their prowess in processing sequential data within the domain of natural language processing, to interpret the complex signal patterns generated by X-ray microcalorimeters. Given the computational intensity of transformer models and the vast datasets involved, our research demands substantial computational resources. The Alvis GPU-focused cluster, with its state-of-the-art Tesla GPUs and Intel Xeon CPUs, presents an ideal environment for conducting the extensive computations and large-scale data analyses central to our project. Incorporating transformer models into the pulse processing workflow promises to significantly enhance the resolution and accuracy of X-ray microcalorimeters, addressing critical issues such as gain drift and systematic errors. The implications of this improvement are profound, with potential applications ranging from sharper astrophysical observations to more precise medical imaging techniques. The success of this thesis hinges on access to Alvis's robust computational infrastructure, enabling the training of sophisticated transformer models on expansive datasets and the processing of extensive simulations. This work not only contributes to the academic knowledge base but also has the potential for widespread practical applications, setting new standards in sensor technology. In essence, this thesis represents a pivotal advancement in applying cutting-edge AI techniques to sensor technology, aiming to surmount long-standing challenges in X-ray microcalorimeter pulse processing and facilitating breakthroughs in both scientific exploration and practical applications.