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Science at Queen Mary University of London, working with Professor Rachel Humphris and Dr Dimitrios Kollias. The successful applicant will undertake computational research, including algorithmic auditing
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and engineers on developing resource-efficient, adaptive learning frameworks for complex data environments. Key Responsibilities: Design and implement ensemble learning algorithms and optimization
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/2027. You will focus on developing microwave techniques and associated electronics to precisely control the curing process, using AI-based algorithms to optimise outcomes. What we're looking
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to machine learning, data analysis, and software development; Assisting in the design of experiments, algorithm coding, and analysis of large datasets; Developing and maintaining websites and web applications
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offloading strategies for IoT networks. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and
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to minimize algorithmic bias. Develop expertise in evaluating AI devices that can adapt and learn post-deployment, including understanding evolving algorithms and creating methodologies to assess algorithm
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capable of supporting and collaborating with humans in complex, real-world settings. You will be responsible for researching and developing novel algorithms and techniques to achieve the project’s
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Fellow to conduct cutting-edge research in AI-enabled modelling and design of microwave and RF devices. The successful candidate will develop advanced physics-assisted modelling techniques to accelerate
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collaborative research projects, develop novel computational algorithms, and contribute to high-quality publication and grant-related activities. The position offers opportunities to work in a dynamic research
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Regular/Temporary Temporary Job Code 0006 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The Garwood lab develops MRI hardware, image acquisition methods, and novel image