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in fluid dynamics, turbulence modeling, CFD, and turbomachinery. Experience with CAD and CFD tools (e.g., Ansys Fluent, CFX, StarCCM+, OpenFOAM). Programming skills (e.g., Python, MATLAB). Knowledge
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This multidisciplinary position is part of a WASP NEST (Novelty, Excellence, Synergy, Teams) project focused on advancing generative models and perceptual understanding in computer vision. The
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to emerging digital technologies Interplay between technology development and business model evolution - how advancements in technologies reshape value creation and value capture, necessitating continous
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. You enjoy combining experimental laboratory work with theoretical analysis and modelling. While your main focus will be the research project and your own development as a researcher, the position also
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utilized to mitigate flooding risks through hydrological modelling and stakeholder engagement.Focusing on the Gothenburg region, the project will: Identify roads suitable for climate adaptation in three
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format. This will allow combinations of neural networks with physics models. The project brings together PhD students and senior researchers from multiple disciplines to tackle challenges in sustainable
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different mixing and reactive properties compared to conventional fuels. In this project, turbulent mixing and combustion of hydrogen in air will be studied through optical experiments and numerical modelling
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with expertise in materials characterisation, computer vision, computational modelling, and machine learning. The other PhD positions connected to the project are: PhD Student Position in Generative
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introduces new and underexplored vulnerabilities to network-based threats. The goal of this research is to uncover such threats, evaluate their impact on training performance and model integrity, and develop
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conduct world-leading research in the development of microwave-based technologies for medical diagnostics, treatment, and monitoring. Our research activities span computational modeling, algorithm