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- SciLifeLab
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conservation, management, and policy. However, we still cannot reliably predict how climate change will affect animal populations across their full life cycle. Accumulating evidence suggests that early life
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prediction, most current models still describe proteins largely as static structures and do not fully capture the conformational ensembles that underlie protein function. This PhD project aims to address
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, audio, and sensor data. These AI models move beyond conventional predictive and purely data-driven approaches by seeking to capture the underlying causal, spatial, temporal, and semantic relationships
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. The processes will be studied using chemical and microbiological analyses, and there will also be opportunities to work with process modelling. Process microbiology. This project focuses on the microbiology
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in predictions derived from medical reports, and on integrating these uncertainties into downstream probabilistic time-to-event models. Applications will focus on prostate cancer, using large-scale