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Field
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A fully funded four-year PhD position is available to work on the project titled “Fault-Tolerant Architectures for Superconducting Qubit Quantum Computers”. This position is a collaborative
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will support the delivery of the vision of this 10-year programme that aspires to develop AI Hardware solutions for Lifelong Learning applications. Your skills and attributes for success: A PhD in
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anticipated that the initial focus of the project will be on transmon design, before extending methods to emerging architectures such as unimons. The PhD will suit a candidate interested in optimisation
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, in natural science and/or social science domains. Candidates should possess a PhD (or be near completion) in PhD in Computer Science, AI, Security, or a related field. You will have a Strong background
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11 Dec 2025 Job Information Organisation/Company KINGS COLLEGE LONDON Research Field Architecture Engineering Computer science Mathematics Physics Researcher Profile Recognised Researcher (R2) First
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develop roots preferentially upslope to provide anchorage against horizontal soil sliding. Greater understanding of how root system architecture and geometry provide resistance to different combinations
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Requirements: Hold at least a Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
such a promising technology, the centralised and resource-intensive nature of current LLMs conflicts with the constraints of aerial 6G networks in terms of limited computation, energy, and communication
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PhD studentship in Trustworthy Multimodal AI under Lightweight and Data-Efficient Architectures Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26
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private. This PhD will focus on three strands of work: 1) Innovate NILM model structures. Design efficient neural network architectures for both aggregator and client models that meet strict accuracy