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Field
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This project explores the use of state-space models (SSMs) for sequence modeling in large language models (LLMs), focusing on their CMOS-based hardware implementation. The PhD candidate will develop energy
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hardware and improving the effectiveness of carbon monitoring tools. If needed, you will be trained to obtain the required expertise in green computing and environmental sustainability, and your work is
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this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping
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into recycle, reuse, or manual-review streams. Iterative hardware-in-the-loop trials will drive rapid prototyping of gripper architectures and autonomy frameworks, thereby establishing a scalable blueprint for
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overall efficiency. The project also involves building a real-time simulation environment, supporting the development of a hardware-in-the-loop (HiL) test bench, and validating the system through field
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. Our approach enhances T2 (Interconnected QC systems) through verification methods for connected networks, supports T1 (Integrated quantum demonstrators) via hardware-agnostic metrics, and enables T3
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/Simulink Experimental studies of power systems utilizing real-time and hardware-in-the-loop tests Insights into regulatory and market frameworks, as well as business models, will be assessed positively. In
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, you will perform benchmarking on materials candidates emerging from CAPeX and collaborative research. Key tasks include: Map out pros and cons of various strategies to upgrade the existing hardware
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hardware is developed in cooperation with Heidelberg university. Data analysis, writing and publishing research articles, and presenting at topical conferences/workshops are an integral part of this project
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This PhD opportunity at Cranfield University invites ambitious candidates to explore the frontier of energy-efficient intelligent systems by embedding AI into low-power, long-life hardware platforms