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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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) simulations will also be performed for the investigations. Furthermore, machine learning can be tested to accelerate MD simulations. In this project, you will be responsible for the following tasks in
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-weighted and functional MRI, intracranial EEG) Multi-scale modelling of human brain development Using machine learning frameworks to interrogate the relationship between brain development and cognitive
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bipedal robot that will learn to walk on soft and natural ground, such as sand and gravel. The controller design will include knowledge of the type of ground the robot walks over, and how the substrate
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based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored, and the findings of your project will be
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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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south of Oslo. via Unsplash Main responsibilities Development and testing of digital solutions for small forest owners. These solutions include Computer vision models and a digital assistant based on a
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, the details of the process are not yet fully understood. Mechanistic learning, the combination of mathematical mechanistic modelling and machine learning, enables a data-driven investigation of the processes
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, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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Your Job: Working with a broad range of imaging modalities (e.g. structural, diffusion-weighted and functional MRI, intracranial EEG) Multi-scale modelling of human brain development Using machine