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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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failure analysis using advanced finite element models and simulation techniques. This is enabled by digital and sensor technologies such as artificial intelligence, computer vision, drones, and robotics
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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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welcoming environment with an international atmosphere that fosters creativity, empathy, teamwork, and outstanding academic collaborations. You will also benefit from strong connections to leading companies
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to the PhD project ie. processing and analysis of dietary intake data, statistical analyses (eg. linear mixed models) as well as evaluation of child growth and body composition data. Relevant publications
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allocation, start-up/shutdown sequences, and operational planning of PtX systems in response to dynamic market and process conditions. System Integration and Digital Twin Development: Combine experimental
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Optically Stimulated Luminescence (OSL) is a multi-step phenomenon driven by the transport of electrons and holes across defects in the crystal lattice. Traditional models rely on simplified coupled rate
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. Responsibilities The role in AM2PM, an EU funded research project, involves conducting innovative theoretical and experimental research in Building Information Modeling (BIM), Digital Twin Construction (DTC
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Senior Researcher in Design and Operation of Sustainable Biomanufacturing Processes - DTU Chemica...
techno-economic evaluation and proven capabilities in the evaluation and modeling of resource recovery and valorization pathways. The role also requires experience in process and supply chain design
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deep understanding of techno-economic evaluation and proven capabilities in the evaluation and modeling of resource recovery and valorization pathways. The role also requires experience in process and