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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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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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approach that simultaneously optimizes both hardware and software for maximum performance per watt. You will investigate how configurable and customizable processing technologies can be leveraged to create
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for their expression in plant colonizing bacteria and integrating them into the chromosomes of appropriate chassis. Control systems will be designed to restrict expression to target plants and ensure optimal expression
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the molecular pathways of human milk production, we aim to resolve breastfeeding challenges and promote optimal long-term health for mothers and infants. Candidate Requirements: We seek a competent individual
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Researcher to join an interdisciplinary team working on a theoretically informed, participatory, implementation study (OPTIM-I), aiming to optimise the use of professionally trained interpreters (PTI) in
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Researcher to join an interdisciplinary team working on a theoretically informed, participatory, implementation study (OPTIM-I), aiming to optimise the use of professionally trained interpreters (PTI) in
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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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• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing