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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive
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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
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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. The group collaborates with
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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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22 Jan 2026 Job Information Organisation/Company Chalmers University of Technology Research Field Computer science » Programming Computer science » Other Engineering » Chemical engineering
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Do you want to contribute to top quality medical research? Interested in developing tools that bridge computational science and nucleic acid technology? Whether your passion lies in computation
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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