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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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cutting in the production facility. Establish a numerical model to simulate the glass cutting process. Design experimental measurements and assist in the integration of sensors in production. Acquire
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and machine learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three
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, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction
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. This knowledge will support the designation of marine protected areas in line with the “30 by 30” conservation target. A central component of the project is the integration and analysis of diverse data sources
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computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics
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mechanics (nonlinear beam theory, fluid-structure interaction) Desire to develop interdisciplinary expertise across hydrodynamics and structural mechanics. Experience with or willingness to learn: Programming
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(ORR), oxygen evolution reaction (OER), and carbon dioxide (CO₂) reduction. Collaborating with theoretical research groups to guide the design of active site structures through computational modelling
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. You should have a strong academic background in engineering, applied mathematics, or computer science, combined with a clear interest in scientific programming, machine learning, and data analytics
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during emergencies and is based on high-temperature PEM (HT-PEM) technology. The HT-PEM stacks operate at approximately 160 °C, offering simplified designs and operational advantages, including improved