49 algorithm-development-"Prof"-"Prof"-"Washington-University-in-St" PhD positions at Cranfield University
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. Expected outcomes include: development of novel algorithms that significantly improve predictive accuracy for equipment failure; creation of scalable monitoring systems that reduce operational costs
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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for Pollinator Monitoring: Train and optimise deep learning models for pollinator detection and classification using annotated image datasets. Post-processing object tracking algorithms will be incorporated
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout
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-disciplinary approach that integrates design, technology and management expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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, such as imbalance and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict
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tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate