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of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In
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Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object tracking, 3D reconstruction, super-resolution. Have a passion on
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mathematical frameworks for multimodal integration, advance video transformer architectures for temporal-spatial dynamics, and develop few-shot learning methodologies for ultra-low-resource scenarios with only
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(IP) using a safe, secure, and reliable video-conference solution (e.g., Zoom) and will las up to 45 minutes. The outcome of the interview will be a written ESR for each candidate. Phase 5. Final
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literature (relative to opportunity), especially on topics relevant to the project objectives demonstrated experience with simulation modelling and coding, especially biophysical modelling advanced statistical
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of MIXAP on teaching and learning. We aim for a qualitative and quantitative analysis with questionnaires for teachers and students, focus groups, videos, usage logs, etc. • Provide a list of
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multimodal data (video, self-report, physiological sensors, automatic facial recognition software) that examines CER in multiple contrasting contexts. And (3) Advance educational theory and practice by
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/Temporary Regular Job Code 9546 Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job We are looking for a quantitative wildlife ecologist to join our team to study how
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concretely your work package contains: We invite you for a fully-funded postdoctoral researcher within the prestigious European Research Council (ERC) Consolidator Grant “Reinventing Multiterminal Coding
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experience developing and adapting qualitative coding schemes based on dialogue and behaviors (from video, audio, eye movements, and log file data), as well as experience working with physiological sensors