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to net‑zero carbon emissions. The project examines how future technologies, propulsion concepts, fuels, operational changes and behavioural shifts can together deliver more sustainable aviation. In
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the neural-network control of a tiled array of fibre lasers in a coherent beam combination architecture, for unlocking novel scaling and beam shaping capabilities in real-time. You will work at the
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warm and recover in a net-zero future. As part of this project, you will (i) quantify global and regional patterns of asymmetric climate recovery using CMIP6 Ramp-Up–Ramp-Down and Zero-Emission
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for spatiotemporal data (e.g., CNNs, LSTMs, Transformers, or Graph Neural Networks). Hybrid modeling: Experience with physics-informed machine learning or the integration of ML with data assimilation/multivariate
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data assembly, software interfacing, test design, statistical analysis, and results reporting. The role will also involve planning and managing project activities, building research skills and networks
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network with data from existing permanent seismic networks in Greece. Data processing will include implementation of automated detection of seismicity and subsequent derivation of a high-quality seismicity
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laser’, through the neural-network control of a tiled array of fibre lasers in a coherent beam combination architecture, for unlocking novel scaling and beam shaping capabilities in real-time. You will
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of their mental models into a machine learning model, using dynamic Bayesian networks to understand, propagate and reduce uncertainty in their assessments. The research will apply models of distributed situation
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, using dynamic Bayesian networks to understand, propagate and reduce uncertainty in their assessments. The research will apply models of distributed situation awareness and ecological interface design
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methods that combine historical climate data, simple physics-based models, and AI to deliver more accurate projections of how our climate will warm and recover in a net-zero future. As part of this project