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? This PhD project offers a unique opportunity to apply machine learning to solve a critical engineering challenge within the railway industry. The Challenge: Rail grinding is a crucial maintenance activity
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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-harvesting complexes. The research will use a combination of quantum and molecular dynamics simulations, electronic structure calculations, and machine learning approaches. These are similar to earlier work
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Primary Supervisor -Prof Michal Mackiewicz Scientific background Marine litter is a key threat to the oceans health and the livelihoods. Hence, new scalable automated methods to collect and analyse
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17 Oct 2025 Job Information Organisation/Company ETH Zürich Research Field Chemistry » Other Engineering » Chemical engineering Engineering » Computer engineering Engineering » Electrical
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information For further information, please contact Prof. Ants Kallaste ants.kallaste@taltech.ee and Prof. Anton Rassõlkin an- ton.rassolkin@taltech.ee or visit https://taltech.ee/en/electrical-machine-group
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, stringent layout design rules demand new design automation solutions beyond the actual state-of-the-art. The proposed work plan focuses on the thorough exploration of innovative generative machine learning
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experience includes: Nano-imaging or sensing methods Optical or vibration detection technologies AI/machine learning for imaging and sensing Background in biology, microbiology, or biomedical sciences
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD