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spatio-temporal regularization, discrete tomography, low-dimensional latent representations and machine learning. The ultimate aim is to reduce the carbon footprint for the construction industry and enable
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on the modelling and optimisation of PRO systems using advanced Computational Fluid Dynamics (CFD) and Machine Learning (ML) techniques. This role offers an exciting opportunity to contribute to cutting-edge
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thermomechanical process simulations such as casting and welding. The research activities at SDU-ME spans widely from fluid mechanics, condition monitoring, machine learning, fatigue, maritime structures
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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. theses at the interface between structural engineering and machine learning. You will disseminate your research through peer-review publications and participation in international conferences. You will
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extraction) that can be miniaturized and integrated into portable devices. Perform SERS measurements and data analysis of SERS data (e.g., using machine learning). Develop, test and apply new fiber-based SERS