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contributions. Demonstrated experience implementing, training, evaluating, or fine-tuning modern machine learning models. Strong programming skills in Python and experience building and maintaining research code
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feedback, and enable independent practice outside conventional instructor-led settings and without audio/video/data connectivity. The selected candidate will join a collaborative environment involving
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team to translate methods to clinical research studies and analyze imaging data. Disseminate findings through peer-reviewed manuscripts, conference presentations, open/reproducible code, and the
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to redefine how machines interpret human actions, anticipate intentions, and interact seamlessly with people in various settings. The successful candidate will be developing new methods for Real-time Video
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multimodal interaction. Our research aims to bridge the gap between visual perception and actionable assistance, with applications including: · Video-based skill coaching and instruction
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difference operators, our focus will be on a Fourier-space description of spatially periodic velocity fields, taking direct inspiration from Fourier pseudo-spectral methods used in standard DNS codes
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collaboration with the pedagogical team and the PhD candidate. Familiarity with multimodal data (audio, video, interaction logs) and time-series analysis of social interaction. Experience with or interest in
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, inspiring education and (inter)national cooperation. Click here to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce
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, and supporting shared infrastructure (code review, documentation, data governance). Uphold the highest standards of research integrity, reproducibility, and responsible use of human biobank data (ethics
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, with part of your time spent on-site at Model Health (Leuven, Belgium) for the development of the video-based prototype. You will work closely with Model Health and Moveshelf, embedded in