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Field
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project entitled “A continually-learning framework for uncertainty quantification and translation of preclinical studies to human cardiovascular safety”. The aim of the project is to develop a statistical
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working with spatio-temporal datasets and remote sensing imagery Knowledge of distributed computing and uncertainty quantification Ability to function well in a fast-paced research environment, set
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for diffusion models and other deep generative models; Uncertainty quantification, robustness, and efficiency in generative modeling; Applications in scientific and industrial domains. Physical Demands and
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time AI-enabled analysis of high-throughput, multimodal experimental data with uncertainty quantification Integration of edge computing, high-performance computing (HPC), and scientific data
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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is decentralized or only partially observable? Depending on the research direction, you may employ techniques from mathematical modelling, machine learning, uncertainty quantification, distributed
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Recovery; Seismic Performance; Earthquake Engineering; Performance-based Seismic Design; Uncertainty Quantification; Reinforced Concrete Structures; Collapse Assessment; Ground Motion Selection; High
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causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records, biomarkers, omics data, registry data, and sensor
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validation, uncertainty quantification, robustness analysis, surrogate modeling, or adaptive feedback/optimization. Excellent written and oral communication skills. Motivated self-starter with the ability
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, and more reliable hybrid thermodynamic and data-driven predictive models The methods developed in this project will overcome current limitations in training data sparsity, quantification of model