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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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modes (e.g., HCCI) for net-zero fuels like hydrogen and ammonia. A key innovative pillar is the development of an AI-driven control strategy. Machine learning algorithms, including reinforcement learning
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built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine
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on the performance of the CMF; Using machine-learning (deep learning) methods to develop a predictive model and conduct the sensitivity study to investigate the multiple factors on the performance of flow meter
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Learning Your role and goals Trustworthy & Adversarial Computing Lab (https://taclab.aalto.fi ) led by Sebastian Szyller is looking for a doctoral researcher (PhD student) to pursue a degree in trustworthy
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, neuroscience, machine learning, or related fields and/or merit/distinction-level performance in a relevant postgraduate degree (e.g. MSc) Experience of working in a neuroscience, clinical or engineering research
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? This PhD project offers a unique opportunity to apply machine learning to solve a critical engineering challenge within the railway industry. The Challenge: Rail grinding is a crucial maintenance activity
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ecosystem services such as carbon storage (1-4). Recent advances in satellite observations and machine learning provide novel opportunities to study extreme fires on a global scale. In a changing climate
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filled. This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end. You will unify modern machine learning, statistical signal
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches