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materials, to aid design of novel more energy-efficient processing routes. The development of these digital twins requires reliable and predictive models for microstructure formation during steel processing
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polymer chemistry or tissue engineering. Strong skill set for data analysis and interpretation, coupled with excellent written and verbal communication abilities. Ability to work effectively in a
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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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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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; 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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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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-throughput datasets generation and analysis, prediction of microfluidic architecture performance, or guided device design. Condition of Employment Reliability Status Language Requirements English Note
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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