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that addresses the complex demands of education in Northern Ireland. By integrating research and practice with a leadership education approach, the programme will be designed not only to strengthen individual
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successful candidate will conduct Density Functional Theory (DFT) simulations and develop machine learning potentials to investigate zeolite-related systems, particularly under complex chemical environments
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Earth system modelling position (15-month duration). The post holder is therefore expected to have prior experience in intermediate complexity Earth system modelling and experience integrating key
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inference techniques. Experience with transformer-based models, LLMs, retrieval-augmented generation (RAG), or foundation models. Experience analyzing complex real-world clinical datasets (e.g., MIMIC-IV
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, to develop solutions for predicting risk in international-scale financial markets. The project is developing event-triggered artificial intelligence approaches that complement existing financial risk models
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network from high-impact, low-probability crisis events. This project addresses critical challenges in dynamic modelling, DT federation, and decision support to help stakeholders manage crises ranging from
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and future-generation detectors, theoretical astrophysics, transient astronomy, gravitational-wave source modelling including numerical relativity, and general relativity theory. The School of Physics
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learning, or computational research Required Experience Demonstrated experience building, training, fine-tuning, and evaluating machine-learning or deep-learning models using established frameworks such as
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• Possess a PhD degree in relevant field, with demonstrated experience in the following such as: • Experimental ballistics tests on rock / cementitious materials • Modelling of high-rate
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integrates clinical, molecular, and real-world data with emerging computational approaches, including large language models, predictive modelling, and multi-omics data integration. The lab coordinates and