A postdoctoral position at Uppsala University focused on rigorous foundations for robust and scalable learning within machine learning theory. The project offers significant scientific freedom, with research themes including robust learning under distribution shifts, optimization theory for large-scale models, and Bayesian inference with PAC-Bayes guarantees. The position also involves research, publishing, mentoring, and possibly up to 20% teaching. Collaboration opportunities exist particularly within life sciences, dealing with real-world data challenges such as noise, missing data, temporal variations, and high dimensionality.