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benchmark the developed framework using project data, high-fidelity simulations including hardware in the loop, and relevant industrial case studies, assessing its robustness and computational performance
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cooling, and two-phase cooling for very high heat fluxes. You will develop and use Computational Fluid Dynamics models in combination with analytical and reduced-order models, and contribute to publishing
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Assistant professor or Assistant professor Tenure Track, Department of Computer Science, Copenhagen Section Department of Computer Science, Aalborg University is hiring Assistant professors
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In this position, you will be part of the research environment within the Solid and Computational Mechanics research group at the Department of Materials and Production in Aalborg. The group works with
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at bachelor's and master’s level, including organizing courses within the Bachelor’s programme in Urban, Environmental, and Energy Planning. You will be responsible for guidance and examination of bachelor's and
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Assistant Professor in statistics for the development of privacy-enhancing techniques in health care
Programme, “Synthetic health data: ethical development and deployment via deep learning approaches (SE3D)” which is a collaboration between Head of Center and Professor Martin Bøgsted, Center for Clinical
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on the emerging organizational, societal and governmental responses to the current transformations of computational practices and digital infrastructures. have comprehensive knowledge of qualitative and quali
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interdisciplinary project funded by the Danish Energy Technology Development and Demonstration Programme (EUDP) and has a strong focus towards commercialization the resulting battery. Your competencies Appointment as
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role in developing and implementing novel computational models within a boundary element framework, coupling the BEM with models for flow separation, viscous drag and cavitation. There is room to shape
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will gain: Strong expertise in statistical and computational methods for privacy Experience working with unique, real-world health data Collaboration with an interdisciplinary research team across data