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Field
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described by patients, there are few interventions that are currently in place to support recovery. To determine how to best intervene in a cost-effective way, we need up to date cost estimates and quality
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promise in understanding disease mechanisms and improving clinical decision-making. Recent studies suggest that generative models can uncover latent structures and improve classifier robustness across
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As part of the Restoration Ecology and Dynamics (READY) Doctoral Focal Award, we invite applications to the following PhD project: Measurement and modelling of future persistence of restored
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, for example, parameter quantization, learning a student LLM from a teacher LLM for down-stream tasks, and design of reversible LLMs. A reversible LLM allows for online back-propagation when updating the model
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breast cancer in animal models (4). In this PhD project, we will investigate: The effect of combined treatment with ITCs and a selected anti-cancer drug (sorafenib or triptolide) on breast cancer will be
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or compromised IoT devices by analysing encrypted traffic patterns, focusing on metadata, flow characteristics, and timing rather than decrypting payloads. The core challenge is creating features and models
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altitude could modify the optical or physical properties of cirrus, but we are currently limited by a lack of observational data. This project will fill that gap, using existing aircraft and satellite
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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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deep generative models, e.g., diffusion, energy based, normalizing flow or transformer-based models. With a focus on the particular domain of molecules. The project will contribute to accelerate the drug
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exhibit complex two-dimensional structures whose performance is governed by subtle variations in layer stacking and nanoscale disorder—features that are difficult to resolve with currently available