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researchers investigating different aspects of multiple sclerosis (MS) pathogenesis. This position offers a unique opportunity to contribute to interactive, clinically driven research by combining a deep
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branches (Soft Materials Modelling, Computational Chemistry, Inorganic Materials Modelling, and Data-driven Atomistic Simulation). The fixed term contract is initially for 2 years and during the first 6
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-assisted image analysis, and AI-driven predictive modeling. The project is part of an EU-wide initiative to address the growing issue of airborne health threats found indoors. Your profile: • Master’s Degree
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a wild, non-model plant species. Your job To face climate change and other environmental challenges, the capacity for rapid adaptation is crucial for both natural plant populations and crops
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” of novel stretchable structures by advancing numerical modelling, data-driven, and optimisation approaches in a nonlinear regime. Project Background Stretchable mechanical structures are a subset of
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workflows to implement such interactions into supramolecular hosts (pubs.rsc.org/en/content/articlelanding/2022/cc/d2cc00532h ). You will learn a wide range of molecular modelling and data-driven techniques
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catalysts used in chemical processes. The successful candidate will combine state-of-the-art quantum chemical modelling alongside machine learning techniques and contribute to the development of predictive
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for: The selected candidate will develop their thesis within one of the following four research lines: Early-age microcracking: monitoring and sensor development AI-driven management of sustainable concrete Digital
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in finite-element analysis Experience in medical image analysis and data processing Basic knowledge of and/or experience in AI-driven models The ability to manage multiple tasks effectively Excellent
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of repeatedly solving wave equations. In the project, we will develop a new mathematical and computational framework that combines PDE-based modelling with ideas from data-driven reduced-order modelling. The aim