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About the position Position as PhD Research Fellow in Machine Learning and Applied Cognitive Neuroscience available at the Department of Informatics. The fellowship period is three years. Depending on the candidate and the teaching needs of the department, the fellowship period can be extended either for compulsory work consisting of e.g., teaching and supervision duties and research assistance up top four years. Starting date no later than December 31, 2026. No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. The position is placed in the Digital Signal Processing and Image Analysis group (DSB), Section for Machine Learning, Department of Informatics at the University of Oslo. The DSB research group has seven full-time and five adjunct positions. We perform research over a wide range of applications in image analysis and machine learning, as well as in digital signal processing and acoustic imaging. There are about 20 Postdocs and PhD research fellows in the group with financing from a variety of national and international funding agencies, as well as from industry. Job description We are seeking an ambitious candidate to develop Machine Learning models and frameworks for time series analysis, aimed at understanding how the human brain encodes information. This cross-disciplinary project is a high-level collaboration between the Digital Signal Processing Group (DSB), at the Section for Machine Learning (IFI), and the Group for Cognitive Neurophysiology (Medical Faculty). The team has research links with Bradley Voytek at the Halıcıoğlu Data Science Institute (University of California San Diego, USA). By bridging experimental neurophysiology with advanced algorithmic design, we aim to significantly enhance the understanding of high-dimensional neural activity patterns. The successful candidate will work with open available datasets obtained in rodents and unique datasets of neural activity. Your primary focus will be to design new learning frameworks and neural network architectures to advance our fundamental understanding of how the human brain forms perception and memories. In detail, you will use transformer architectures to analyze time series of local field potentials recorded in rodents and compare performance to time series of action potentials recorded in the same animals (the state of the art); apply and interpret the architecture to local field potential data recorded in humans who have seen a vast number of images from the CoCo-database ( https://cocodataset.org ); and apply and interpret the architecture to local field potential data recorded in humans who have seen movies. The data for the project is already collected and available. Project leaders are Adin Ramirez Rivera (DSB, IFI) and Jørgen Sugar (Medical Faculty). What skills are important in this role? The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials. Required qualifications: Master's degree or equivalent in computer science, physics, applied mathematics, electrical engineering, cybernetics, data science, computational science, neuroscience or related fields For applicants with a foreign completed degree (master level), it must be equiva-lent to a master in the Norwegian educational system Documented proficiency in scientific programming (e.g., Python) Documented proficiency in deep learning frameworks (e.g., PyTorch) Documented background in machine learning, mathematics, linear algebra, and statistics Fluent oral and written communication skills in English Desired qualifications: Experience with transformer models and attention mechanisms Strong background in probabili