26 computer-algorithm "Integreat Norwegian Centre for Knowledge driven Machine Learning" positions at Linköping University
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. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as
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evaluating efficient and scalable techniques for systems that process and answer such queries (e.g., query optimization algorithms, adaptive query processing approaches). Conducting this research work includes
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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decentralized machine learning in 6G networks, and design machine-learning algorithms that can handle the network imperfections that remain impractical to resolve at the physical layer. The focus of the research
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application! We are looking for a PhD student in Statistics with placement at the Division of Statistics and Machine Learning, Department of Computer and Information Science. Your work assignments As a PhD
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models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning which clearly integrates the two subject areas within the division
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, where AI models are trained without having all data in a single computer. This makes it possible to use larger datasets for training, without sending sensitive data between hospitals. The goal is to
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application! The position Linköping University has a new cybersecurity lab that includes both a computer room for students and a server environment that can be used to simulate different scenarios related
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Chemistry » Computational chemistry Physics » Applied physics Researcher Profile Recognised Researcher (R2) Positions Other Positions Country Sweden Application Deadline 20 Jan 2026 - 23:31 (Europe/Stockholm
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cyber-physical systems (CPS). ESLAB’s work spans advanced system design, dependable computing, and emerging applications in autonomous and safety-critical domains. As an associate professor in Cyber