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through long-term impact assessment and optimization. The goal is to develop a framework to estimate carbon emissions across AI's development, operation, and use. This framework enables stakeholders
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identified in other WPs. The model construction will be informed by qualitative and quantitative knowledge of supply chain processes through dialogue with stakeholders, and model parameters will be estimated
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becomes essential. This project will focus on building a comprehensive digital twin of a future quantum computer to investigate how classical subsystems scale and interact, and how this scaling impacts
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challenge, making energy-efficient computing a critical research priority. This project addresses this challenge through a novel co-design approach that simultaneously optimizes both hardware and software
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research hub (HI-ACT) funded by EPSRC. The project seeks to deliver industry-relevant tools that enable optimal design and operation of hydrogen technologies in real-world application. The successful
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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inland, short-sea, and high-seas shipping routes. The project seeks to deliver industry-relevant tools that enable optimal design and operation of greener vessels, backed by real-world demonstrations
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in at least two projects related to scale up and optimization of biomass valorization processes from wood and agriculture feedstocks for textiles and packaging applications. The portfolio of projects
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applicants for a 6-month paternity leave replacement who have a strong interest in using computational methods such as cognitive and psychophysiological modeling, (Bayesian) statistics and optimal experimental
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national and international partners Maintain, calibrate and troubleshoot high-end mass spectrometers with a focus on GC-HRMS and Orbitrap analysers Develop, optimize, and validate analytical GC-MS and LC-MS