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Field
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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-informed learning) with hard physical constraints (Navier–Stokes in spectral space) we will develop methods to super-augment experimental data via data assimilation and turn sparse wind-tunnel measurements
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an increasingly complex development environment. Areas to consider that impact the modelling are: Framework Language Process How wide / how deep i.e. what do we model and why? How much provides a good answer i.e
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perform specialized fabrication and experimental tasks and develop a deep understanding of the theoretical framework and modeling tools. This will require communication skills, capacity to learn, and
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heating and cooling, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting
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focuses on advanced methodologies in abdominal imaging, particularly applications of machine learning and deep learning to medical image analysis. The lab aims to advance existing imaging techniques and
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is expected to have a profound knowledge on most of the following topics: Robot control Deep Learning Medical imaging Preferably, the candidate has experience with: Robotic simulation tools Medical
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at the intersection of AI and advanced electron microscopy. The project focuses on developing novel self-supervised and physics-informed deep learning methods to restore and denoise Transmission
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autonomous driving. Your profile Master's degree in Computer Science, Artificial Intelligence, Robotics, or related field Strong background in machine learning, deep learning, or computer vision Experience
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the maximum impact. Develop a novel approach to recycling the material to enable the use of additively manufactured waste as an alternative feedstock. Gain a deep understanding of the sensitivities, reliability