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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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levels, - policy design and impact analysis, - resource allocation, growth and welfare evaluation, - technology diffusion and firm level innovation. leading to an internationally competitive PhD degree and
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considered an advantage. Technical Skills Proficiency in MATLAB, Python, and/or R. Experience with data science frameworks (e.g., AI, LLMs). Familiarity with machine learning (e.g., scikit-learn, MVPA, RSA
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28 Oct 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile
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This PhD project aims to achieve a breakthrough in thermal efficiency (>50%) for dual-piston Free-Piston Engine Generators (FPEGs). The research will pioneer a novel, integrated methodology
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Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 29 days ago
, virtual reality, biology and medicine. Using unique 3D & 4D capture facilities, machine learning, computer vision and advanced graphics, we are modeling humans and animals shape and behavior. In the Eye
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international experience during their PhD. We offer a PhD student position to explore and develop Machine Learning models evolving over space and time and to make use of these models to understand
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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 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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reference architecture for data visiting. This paradigm enables algorithms to securely access and process data within the environments where it resides, supporting federated learning for training machine