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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
for predictive modelling and state estimation for fundamental applications within physical sciences. Your role The main research responsibilities involve building cutting edge machine learning techniques
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strategic raw material (CRM) deposits within Paleoproterozoic cover sequences that overlie an Archaean basement. Existing structural models for the cover rocks predict that crustal-scale fault and shear zone
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variants as functional and assess their impact on gene expression. Contribute to large-scale modeling of engineered traits to predict performance and optimize design. Required Qualifications: PhD in
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attention to scientific rigor and interpretability Experience with XAI tools (SHAP, LIME, Integrated Gradients) to identify which features of the model are driving the predictions Clear written and verbal
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transfer This research combines advanced numerical simulation and artificial intelligence to develop predictive models for high-temperature multiphase flows, with specific relevance to steel casting
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the integration of behavioural data with AI. The student will analyse eye movements, exploration patterns, and verbal reports to develop computational models that predict identification reliability
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of switchable RNA nanostructures. Develop databases for RNA modules for automated building of atomistic models. Develop multistate sequence design algorithm for rational design of RNA switches. Develop database
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and validation of a predictive pipeline for excipient–biologic interactions Integration of experimental SAXS data with AI-driven structural modeling to predict oligomerization behavior and excipient
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simulations of compact binaries (including, for example, binary black holes, binary neutron stars, and black hole–neutron star binaries). The broader goals are to generate accurate predictions for gravitational
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patients with early DKD and matched healthy controls. ▪ Development of an AI-driven digital African twin model capturing population-specific molecular heterogeneity, disease trajectories, and predictive