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high-quality research in one of the Department's key research areas: (i) Artificial Intelligence and Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition
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Machine Learning; (ii) Big Data and Data Management; (iii) Computer Vision and Pattern Recognition; and (iv) Distributed Systems and Networking. These key research areas have a special thematic focus on (a
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and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from chem- and bioinformatics to computer vision and social network analysis. Machine learning with
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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to develop novel machine learning methods to improve malware detection. The doctoral student position is offered within a research project financed by the Wallenberg AI, Autonomous Systems and Software Program
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Computing Science at Umeå University is looking for a doctoral student in machine learning
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science, data science, applied mathematics, physics, materials science, or a related field. Solid background in machine learning and/or computer vision. Interest in representation learning, active learning
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engineering (focusing on deep learning for computer vision), and the division of statistics and machine learning at the department of computer and information science (focusing on the theory behind machine
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applicants should have a strong academic record with a solid background in Machine Learning. Knowledge of Vision-Language-Action models and Novel View Synthesis techniques is a strong plus. Good programming