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. The research should focus on low-power embedded systems, multimodal sensing (including wearable shoe-based platforms), and edge-cloud computing with serverless and federated learning techniques. You will work
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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 systematic phases
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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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to lifelong learning students. We have about 300 dedicated employees. Read more about us at www.energy.dtu.dk . Technology for people DTU develops technology for people. With our international elite research
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), aiming to bridge neuroscience and electronics. The project integrates expertise from circuit design, machine learning, and neurotechnology to deliver innovative solutions for applications such as brain
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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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 be able to work as part of a team to achieve ambitious goals Teach and co-supervise BSc and MSc student projects Potentially participate in Arctic field campaigns We expect that you have: Experience
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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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degree at the highest international level. You will become part of an active learning environment, in Denmark as well as abroad, and you will obtain an extensive academic knowledge e.g. by participating in