38 pattern-recognition "https:" "CMU Portugal Program FCT" PhD positions at Cranfield University
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at the edge. The project explores advanced topics such as TinyML, neuromorphic design, reconfigurable logic, and autonomous fault recovery, with applications ranging from aerospace, energy, and robotics
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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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sectors like aerospace, healthcare, and manufacturing. The convergence of AI with fault-tolerant design principles is transforming traditional maintenance paradigms, leading to more robust and intelligent
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. You will quantify how spatial patterns, network structure and environmental context influence the capacity of blue spaces to provide co-benefits such as biodiversity support, cooling, air quality
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seeks to enhance the predictivity, accuracy and applicability of FEA for WA-DED, enabling more efficient design and control of large-scale additive manufacturing processes. The student will be based
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candidate would have experience with computational modelling and control of dynamical systems. Other useful skills include scientific programming (e.g., Python or Matlab), control system design, and
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ecological resilience and community wellbeing. You will quantify how spatial patterns, network structure and environmental context influence the capacity of blue spaces to provide co-benefits such as
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design, technology and management expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base of manufacturing research. The Integrated
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data demands new statistical thinking and methods. As data size increases, each feature and parameter also becomes highly correlated. Then, their relations get highly complicated too and hidden patterns
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shifts, and stringent latency demands render traditional beam management ineffective. This project will design, implement, and validate an AI-native predictive beam-steering framework that combines orbital