I am a PhD student at Halmstad University's School of Information Technology, supervised by Universitetslektor Sepideh Pashami and Professor Slawomir Nowaczyk. My research within the KEEPER project focuses on developing foundational AI/ML models tailored explicitly for time series forecasting in industrial applications. My role involves designing and implementing novel algorithms for analyzing high-frequency, largely unlabeled data streams from complex industrial systems. Due to the nature of industrial datasets—characterized by sparse and unreliable labels—my work emphasizes self-supervised, semi-supervised, and meta-learning approaches. I aim to create robust forecasting methods capable of capturing latent operational patterns, enabling effective anomaly detection and predictive insights. To support these computationally intensive tasks, I am seeking access to Alvis at Chalmers University of Technology, an infrastructure specifically optimized for AI research. Alvis’s GPU resources and scalable storage capabilities are essential for efficiently training and evaluating large-scale deep learning models integral to my research. This project aligns closely with NAISS's goals to facilitate high-impact AI research in Sweden.