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Supervisory Team: Dr. Jie Yuan, Dr. David Toal PhD Supervisor: Jie Yuan Project description: Robust design is crucial to ensure the durability and reliability of aerospace components throughout
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area “statistics to serve society” which focuses on developing statistical methods and software for the analysis of large and complex collections of data. The PhD student(s) is planned to be connected
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behind the catalytic properties and performance. Aims The successful PhD candidate will tackle key technologies in this project to develop efficient and reliable anodic catalysts for ammonia oxidation
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to the development of multiscale computational models for simulating crack propagation and establishing reliable methods to predict the residual strength of composite structures. The simulations, performed in Ansys
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; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time-to-event analysis; and statistical machine
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research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle Analysis
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cooperation with Kopter Germany GmbH and the Engineering Risk Analysis Group of Prof. Straub, which provides information on both the health and the actual stress of helicopter components. For this so-called
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This PhD project will focus on developing, evaluating, and demonstrating an intelligent solution of diagnosis and prognosis for rotating machinery to enhance safety, reliability, maintainability and
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Privacy-Preserving and Reliable Artificial Intelligence to be occupied starting from the 01.10.2022. The Trustworthy and Privacy-Preserving AI Group (PI: PD Dr. Georgios Kaissis) at the Institute for AI in