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in a randomised, placebo-controlled design framework. Biological ageing is associated with a progressive decline in mitochondrial efficiency, NAD⁺ availability, skeletal muscle function, and
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. Experience of working with large multimodal datasets. Interest in human-computer interaction and human-centred system design. Strong communication and organisational skills. While is not necessary to have
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prediction to systems that reason, plan, interact and act in the physical world. This PhD addresses efficient long-horizon task execution in Physical AI—complex tasks needing sequences of decisions, subgoals
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and sub-ns timescales and conversely how magnetic switching could influence the dynamics of photoexcited states, which would bring considerable progress in the design of performing materials
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face of adversity. Show curiosity and a strong motivation for the subject. Analyze data, assess different perspectives and draw well-founded conclusions. Be flexible and open to adjusting the plan
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technologies. As the design principles for long-lived molecular magnets become clearer, the next challenge is assembling these units into spin lattices via surface deposition and addressing their magnetic
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into finite-volume numerical implementations for the general predictions of laser interactions with substrate materials The plan is to recruit a PhD candidate to undertake this project and be part of a new MoD
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requires overcoming coupled challenges in device switching behaviour, converter design, electromagnetic interference, thermal management, and long-term reliability. This PhD project will investigate advanced
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). The uncertainties in these projections are dominated by uncertainties and poor representation of the interactions between the Antarctic Ice Sheet (AIS) and the ocean. AIS flows from the continent into the ocean
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agencies respond to incentives designed to maintain or enhance RD. This framework will capture feedbacks between ecological dynamics (e.g. functional trait variability, species turnover, co-tolerance) and