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Tommy Löfstedt is an Associate Professor in the Department of Computing Science at Umeå University, specializing in machine learning and computer vision, particularly in applications related to medical image analysis and life sciences. He has over a decade of experience in artificial intelligence, with a focus on the subarea of machine learning. His research involves developing novel methods for automatic segmentation and regularization in machine learning to enhance the interpretation of results. Currently, he is actively recruiting doctoral students in the field of Bayesian federated machine learning and offering master's thesis projects in medical image segmentation and reinforcement learning. Löfstedt leads research projects that aim to improve treatment methodologies in pediatric oncology and optimize plant nutrient uptake using advanced AI techniques. His work has implications for personalized radiotherapy and medical imaging advancements, demonstrating a strong commitment to innovation within these domains.
Umeå University • Umeå
Teaching and conducting research in the field of computing science, focusing on machine learning and computer vision.
Requirements are standard for Master's programs across Social Sciences and Humanities at Umeå. English 6 proficiency is the general rule.