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: You will be responsible for the sensor system and the perception algorithms of an autonomous mobile robot. You will engage in research around deep learning and 2D/3D computer vision for a well-defined
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head-on. We will reinvent generative cooperative vision and semantic compression methods so fleets of intelligent machines can perceive the world robustly, efficiently, and in a trustworthy manner—even
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for advanced courses, international research visits, and networking across Sweden’s top universities. Information about the research group The Computer Vision Group at the division of Signal processing and
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systems, and infrastructure development, contributing approximately £100 billion to the UK’s GDP. While emerging 3D printing (3DP) technologies offer promising opportunities for product individualization
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for culture and creative domains. These domains may include sound, music, visual art, 3D graphics, movement, or multimodal combinations thereof. The research will employ a mixed-methods approach: the search
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maps and model building. You should have a Ph.D. and have more than 2 years of experience in cryo-EM data processing and 3D map generation. You will work under the guidance of Dr. Maria Schumacher on a
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meet the requirements for admission to the faculty's Doctoral Programme (Phd - NTNU ) Software skills in 2D/3D technical drawing Strong theoretical background in fluid mechanics, mathematics and physics
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electron microscopy analysis, Raman spectroscopy, fluid inclusion analysis, potentially appropriate petrochronological methods, and 3D geological modelling. The project will be conducted in partnership with
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taking our Match My Skills quiz. Research Why QUT Explore Our research vision Equity and diversity Support for researchers Meet our experts Ethics and integrity Research jobs at QUT Our facilities
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The objective of this project is to integrate novel vision and AI based techniques for developing digital twin models for structural health monitoring. Computer vision based 3D displacement measurement, crack