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Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position
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. Together with variations in processing parameters, this complexity makes it challenging to ensure that specific material properties remain stable from batch to batch. You will explore how Machine Learning
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to the VIC model. The candidate will develop novel strategies to simultaneously calibrate parameters and assimilate multiple satellite and in-situ observations of streamflow, soil moisture and groundwater
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dissemination is expected to focus on leading Human-Computer Interaction venues. For further information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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or Computer Science; Human-computer Interaction, Spatial Cognition or related areas; Engineering, Applied Mathematics, Statistics or another Quantitatively Oriented Discipline. Application procedure Your complete
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis