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that lead to lower infrastructure demand in the long run and reveal pathways to turning points in infrastructure use through a systemic modelling approach. Essential activities within your project will be
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handling, enabling first-time-right manufacturing. The predictive quality of these tools relies on accurate constitutive models that describe the behavior of the molten material during forming. With
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will develop and test a Washing Detection Model to better understand when and why consumers are vulnerable to washing. In the second phase of the project, you will design and evaluate a model-based
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models to simulate gastrointestinal navigation and refine the system’s interface and functionality based on clinician feedback. Contribute to preclinical deployment where the system will be used in early
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, integrating AM into supply chains demands new models and methods. In this PhD project, you will: develop dynamic supply chain design models that enable the repositioning of AM equipment based on Defence
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environments. The project will build on an interdisciplinary approach, combining concepts and methods from physics, chemistry, and mathematical modelling. The long-term goal is to derive design principles
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, Cascais, Riga, Vilnius, Melsungen, Ciampino, Urla and Rhodes. The PhD project will involve: The use of data analytics (statistical models, machine learning, uncertainty quantification) to monitor and
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well as their cleanroom fabrication by silicon micromachining will be investigated. The main challenges are (1) the design and modelling of new sensor topologies, (2) development of the MEMS fabrication processes, (3