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Postdoc in Application of eXtended Reality for Inclusive Automated Vehicle and Road User Interaction
, supportive environment that fosters learning and professional growth. Job requirements Hold a PhD degree in a relevant field (e.g., Transportation, Human-Computer Interaction, Computer Science, or a closely
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Physics Informed Machine Learning method which exploits the advantages of physics-based and data-driven models, while mitigating the disadvantages. This research will contain experimental and modelling
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energised — not deterred — by problems that sit between physics, learning and the messy real world. Your experience and profile: a PhD (completed or near completion) in Machine Learning, Computer Vision
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23 Sep 2026 Job Information Organisation/Company Delft University of Technology (TU Delft) Research Field Environmental science » Global change Environmental science » Water science Geosciences
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. Our community is open and informal, with more than 7,000 students, 1,000 PhD students, and 1,400 staff members from all over the world. If you would like to learn more about the Faculty of Science and
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. Our community is open and informal, with more than 7,000 students, 1,000 PhD students, and 1,400 staff members from all over the world. If you would like to learn more about the Faculty of Science and
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inside and outside our research group to stength the research outcome. Job requirements A PHD degree in a relevant subject, such as aerospace engineering, aeronatucial engineering, atmospheric sciences
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packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in
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competencies Education You should have completed within the past five years, or be close to completing, a PhD in aerospace engineering, mathematics, physics, computer science or a related field. A PhD in space
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executed. In close collaboration with PhD researchers and project partners from TUM and ETH, you will contribute to the development of novel control and learning methods for aerial manipulators and multi