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Filip Ekström Kelvinius is a PhD student at Linköping University, specializing in the area of machine learning, with a keen focus on deep learning methods applied to graph data. His research primarily aims at discovering new materials, motivated by application in various fields. During his PhD, Filip has been deeply involved in method development for machine learning, particularly in crafting discriminative models such as graph neural networks to predict the molecular properties of materials. More recently, his work has shifted towards generative models, including those known as diffusion models, reflecting a growing interest in innovative methodologies for material discovery. His interdisciplinary research bridges the gap between advanced computing techniques and practical applications in material science.
Requirements are standardized across the Faculty of Science and Engineering (Institute of Technology) for international Master's programs.