69 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof" positions at Cranfield University
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. Focusing on adaptive intelligence, which blends human creativity and machine intelligence, the project will develop Multi-Intelligence Agents (MIAs) to facilitate the seamless integration of social factors
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project, in collaboration with Darvick Ltd. , and mainly based at their headquarters near Birmingham. Will develop a novel, standardised hydrogen embrittlement and permeation testing method to accelerate
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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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for sustainable waste management and resource efficiency in free-market economies. The primary aim of this project is to develop a sophisticated ensemble prediction model specifically tailored to address
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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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vulnerabilities like side-channel attacks and unauthorized access, which can compromise system integrity. Developing robust security measures within AI-enabled electronics is essential for applications in defence
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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
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are critical especially around congested or critical infrastructures. This research aims to develop decision making and planning algorithms that can mitigate the risks challenging environments of AAM
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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