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applications. Teach and supervise BSc and MSc student projects. The research activity will be carried out within the framework of the Center of Excellence – IDUN. In IDUN, our motto is ‘bringing science to life
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processing and hybrid BCI design Machine learning (ML) Bioinspired control systems Neuroplasticity and motor recovery Real-time control of soft exoskeletons Your Role As a PhD candidate, you will: Develop and
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are looking for candidates who have experience with developing AI or machine learning models, as well as bacterial sequence analysis. You should be familiar with relevant programming languages such as Python
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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness
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, PhD 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
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was established to create value for and with society. Whether our contributions come in the form of excellent research, innovative solutions, education or learning, we must make a positive difference to society and
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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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opportunities for lifelong learning. Complex challenges call for joint action, and therefore our strategy focuses on strengthening current and starting new partnerships with other sciences, the business community
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning