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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
research on the integration of Digital Twins with AI/ML technologies for infrastructure lifecycle management. Develop and validate computational models for monitoring and predicting infrastructure
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fields). Strong quantitative skills and demonstrated expertise in predictive modeling and advanced computational methods (e.g., Multilevel Vector Autoregressive Models, Dynamic Structural Equation
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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effectiveness and toxicity of the treatments. Other duties: Develop and validate cancer risk prediction models using deep neural networks based on semistructured data. Develop and validate learning strategies
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to combine high-throughput metabolomics with 3D cell culture models Perform large chemical and genetic studies in cancer cell lines derived spheroids Develop predictive model of drug response by comparing 2D
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the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will
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of whole plants at crop level. A central element is the plant’s 3D geometry, and models should predict plant growth, development, and yield as well as key physiological relationships across the whole plant
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selectivity and permeability and ultrahigh water permeability combined with high salt rejection. The objective of this work is to construct atomistic models of MOFs/Polymers and Artificial Water-Channel
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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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