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: Textual Prediction of Survival (LLM classification & Attention Modelling) This project develops a model to predict patient survival by analyzing heterogeneous clinical documents. Unlike traditional methods
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review, Asana and JIRA for agile project management, and Qlik Cloud, Snowflake and Posit Connect for deploying dashboards and predictive models to support the University’s sophisticated fundraising
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field. This approach is related to data assimilation, allowing for better prediction, control, and optimisation of turbulent systems in engineering, energy, and environmental applications
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plants with material co-production in energy system optimization models including, e.g., reservoir productivity predictions, novel surface processes for CRM extraction, CO₂ reinjection, and reconversion
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Learning-enabled control and reinforcement learning Power system operations, planning, and electricity market design Transportation systems modeling and optimization Responsibilities: Postdoctoral fellows
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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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intelligence as applied to trauma systems and acute care surgery. Fellows will engage in cutting-edge research spanning multiple domains, including risk prediction models for surgical complications, clinical
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of pharmaceutical formulation and manufacturing processes. The role The post holder will develop and implement mechanistic models to analyse and predict the behaviour of pharmaceutical processes. Your work will
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, prior distributions and posterior predictive checks, model comparison, programming in R (python/Matlab), implementations using R-packages rstan/JAGS and brms/STAN or equivalent interfaces. References
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mechanisms of adaptive and acquired drug resistance, exploring network-level control and feedback in cell signaling systems, identifying novel drug targets and therapeutic strategies, and developing predictive