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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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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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differential equations relevant to computational fluid dynamics. These efforts might include Bayesian physics-informed neural networks and neural operators. Bayesian neural networks for approximating piecewise
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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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NAISS, the National Academic Infrastructure for Supercomputing in Sweden, provides academic users with high-performance computing resources, storage capacity, and data services. NAISS is hosted by
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. The workplace You can read about the workplace here: Linköping University, https://liu.se/en Theoretical Physics Division, https://liu.se/en/organisation/liu/ifm/teofy Theory of disordered materials unit, https
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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
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2026 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within
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Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon 2020 Is the Job related to staff position within a Research Infrastructure? No Offer Description
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes