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Field
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: formulate and analyze stochastic models of evolving populations using methods from statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions
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applied mathematical modelling machine learning multi-fidelity modelling numerical methods. Demonstrated programming ability (MATLAB/Python/C++) and enthusiasm to learn PyTorch. Previous experience in one
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failure (if they can), but they cannot explain why it happened or calculate the exact contribution of each individual stressor. Furthermore, these models often fail and make overconfident predictions when
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I supervise projects in particle physics. My main emphasis is on phenomenology, comparison of predictions with experimental measurements. I follow developments in flavour physics: weak decays
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statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions to genomic and epidemiological data; design controlled computational experiments
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of energy and computational power. Reservoir computing offers a promising alternative by using complex physical systems to perform tasks such as prediction, classification, and signal processing. However, one
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the service life of infrastructure. The research content includes: real-time data analysis and modeling pipeline network dynamic prediction and state identification optimisation algorithm and high-performance
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Water consumption under various application parameters Impact of dosing changes on crop quality Economic efficiency Impact on variability of soil properties Predictions based on AI models Supervisor
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If you want to pursue a research career at the intersection of additive manufacturing (AM), microstructural engineering and advanced statistical/machine-learning (ML) based modelling, then this PhD
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Sustainable Healthcare Decisions. About the project The fellowship period is 3 years and devoted to carrying out a project entitled “Reliable Bayesian prediction models with uncertainty quantification