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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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the PhD student in high-performance computing, computer programming, applied mathematics, fluid mechanics, mathematical modelling and data analysis for large datasets -of the order of 100 Terabytes
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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fluorescence microscopy to follow in real-time how nanomedicines engage with living cell membranes (and simpler model systems) and quantify these interactions. Similar studies can be performed for understanding
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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with five partners. The focus of the doctoral program is the analysis of the spatial and temporal variability of decontamination efficiency in different soil materials on a flow cell scale. The results
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data to decode multisensory information Investigate how neural representations change across different brain states (awake, asleep, engaged) and track representational drift over extended time periods
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species and vegetation ecology), advanced statistical modelling using R software, and conducting fieldwork under harsh environmental conditions. A successful applicant should have good skills in English and
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on these comparisons, you will create agent-based models (ABMs) that define interaction rules based on observed similarities and differences in events [4], with a focus on the specific role of individual differences
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aims at developing tools using large language models (LLMs) for the correction of misinformation about climate change in social media. The successful candidate will develop innovative tools leveraging