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Field
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of multimodal prediction models to detect and predict atrial fibrillation (AF) and other clinically relevant cardiac rhythm patterns, predict disease progression and treatment response, and support personalised
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observational constraints (e.g. atmospheric or isotopic observations) to improve model predictions; enhancing model capabilities (e.g. nitrogen-constrained methane production); assessing the importance
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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R or Python programming Tissue culture techniques Animal models Interest in translational cancer research, immunotherapy, and neuro-oncology. Other Requirements Ability to work with patient-derived
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machine learning to rich molecular data and uncover the molecular basis of IBD. The successful candidate will build prediction models from high-dimensional molecular measurements, with a particular focus on
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where possible This post is to work on the Horizon funded project SaliNova: Advancing Europe’s Capacity to Monitor, Predict and Manage Soil Salinization for Adaptation and Mitigation with Novel Data and
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by ATi/Innovate UK and Rolls Royce and is fixed-term to October 2029. You will join a world‑leading programme advancing experimental and numerical methods for predicting the performance of 3D woven
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the mechanisms of immune regulation in sepsis, with a focus on neuroimmune and interorgan communication. It is also desirable for the candidate to have a strong interest in modelling gene regulatory
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of large-scale geospatial and time-series datasets. The candidate will develop and evaluate multimodal AI models to characterize vegetation and land-surface dynamics and quantify ecosystem responses and
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. Familiarity with causal inference, statistical learning, clinical prediction modeling, or real-world evidence methodologies. Experience developing reproducible research software, computational pipelines