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properties of skeletal muscle during static and dynamic contractions. The student will also participate in early-stage algorithmic work to model muscle architecture and behavior across contraction types. In
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wearable and ambient IoT sensing systems for activity and health monitoring. Implementing embedded AI models for anomaly detection and behaviour analysis. Working on digital twin and serverless IoT
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restraint conditions. A key goal is to develop both a sensor system and a prediction model for the short- and long-term deformation behaviour of concrete. These tools will be applied to full-scale structural
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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models
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properties of the extracted compounds, (iv ) scale-up the optimized extraction process for potential industrial application. This is a unique opportunity to contribute to sustainable food innovation while
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sustainable materials, (d) Artificial Intelligence (AI) models to predict and control the manufacturing process and (e) a Digital Twin (DT) incl. Building Information Modeling (BIM) information backbone
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Foundation Models initiative . The proposed starting date is 1 September 2025 or soon thereafter. The appointment will be made for a term of three years at a competitive salary and will follow the PhD study
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of the following areas and an interest to develop within others: Protein chemistry Enzyme kinetics and kinetic modelling Experimental physical chemistry Electrochemistry Assay development and
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. Your work will focus on developing physics-informed AI methods to enhance decision-making in design and operation of next generation thermal energy storage systems, such as latent heat TES and
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designs, building effective and conceptual models to inform our theoretical understanding, and developing code and theory frameworks to address new topological phenomena. Depending on the project’s results