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Computer Vision algorithms. Experience using urban building stock modelling and urban digital twins What you will do: Design & Develop: Create data structures for detailed, spatialised construction component
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application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including
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. This involves formulation, implementation, and validation of novel hybrid models. The study emphasizes methodological innovation, scalable algorithms, and translation to industrially relevant multiphase reactors
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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer
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6 Mar 2026 Job Information Organisation/Company IFM/Linköping University Research Field Physics » Computational physics Researcher Profile Established Researcher (R3) Application Deadline 30 Mar
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included. Eligibility A person who has been awarded a doctorate or a foreign qualification deemed to be the equivalent of a doctorate in mathematics or computer science or comparible is eligible
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this project, we will develop new algorithms and computational schemes as well as further develop existing computational frameworks in the team. We will focus on two related frameworks in the project
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they can contain traces of the data used in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we are looking for The following requirements are mandatory: A doctoral