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classification. To study how to incorporate expert feedback into a semi-supervised learning model, and how to efficiently compress and run the model on embedded devices. To combine heterogeneous data streams
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mitigate hallucinations in video-based LVLMs for autonomous driving. Objectives Design, develop, and evaluate novel method(s) to detect and localize hallucinations in LVLM outputs for autonomous driving
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. This will involve investigating techniques for model compression and efficient inference to enable on-board condition monitoring directly at the wind turbine, reducing data transmission requirements, central
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(e.g. PDF, web) or equivalent evidence (e.g. computer programs, video, design artefacts, publications) demonstrating the technical and creative competencies listed above.Short-listed candidates will be
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, publications, …), 2) contact info of two references together with 3) a strong motivation letter, and 4) a weblink to a short video introducing yourself. The selection committee reviews new applications on a bi
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, and 4) a weblink to a short video introducing yourself. The selection committee reviews new applications on a bi-weekly basis. If you are eligible after review, you will be invited for an interview