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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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computational methods to optimize wind farm performance, which in turn generates clean energy that can power future HPC systems. What you would be doing: You will design, implement and test energy-efficient
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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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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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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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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emphasis on research infrastructure and technology rather than preparation for an academic career path. You will be involved in research, but more focused on learning and improving how computing, workflows
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buildings closed days (and Christmas Eve when it falls on a weekday) for all full time staff. Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm to see the value
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solutions ; High -performance computing systems to support specialized AI and LLM, and to empower the development and refinement of advanced algorithms for 6G communications, optimal grid management
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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