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the investigation and realization of improved microwave probe design, data processing, and visualization techniques to provide a robust method of data analysis, flaw characterization and sizing. AI/machine learning
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), computation (bioinformatics, machine learning, statistical analysis), working with animals (radio-tracking, animal handling/sampling), and deep knowledge of evolutionary biology and gerontology. The Norwich
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for their employability in applications. Additionally, machine learning methods need to be applicable to high-dimensional and to noisy data that are typically encountered in real-world applications. The aim of this project
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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD
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, biology, or a closely related discipline Desirable experience: optics and photonics, AI/machine learning, biology, or biomedical sciences Excellent English, analytical, and problem-solving skills UK
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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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. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience studying bumblebee behaviour
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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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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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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