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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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scientists covering a broad range of expertise in photonics and electronics. The Project in Short The project focuses on developing numerical modeling and optimization tools to explore the information
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available at the Structural Virtual Testing and Digitalization section at the Division of Materials and Components of DTU Wind and Energy Systems. The section’s expertise lies in multi-scale progressive
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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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the biochemical, physicochemical, and techno-functional properties of the extracted material using state-of-the-art facilities. Scale up the extraction methods using the advanced facilities available
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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
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an efficient AI foundational exploration of the molecular space. How can we bias the generative models towards desirable molecular properties How can we integrate generative AI models and different molecular
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, electrolysis, power-to-x, batteries, and carbon capture. The research is based on strong competences on electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery, materials
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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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