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Field
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, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection or analysis. Familiarity with deep learning frameworks (PyTorch, TensorFlow). Experience working with multimodal
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Knowledge of MLOps concepts, including dataset ops, training pipelines, evaluation frameworks, deployment and monitoring. Understanding of deep learning fundamentals and hands-on experience with model
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intelligence (including machine learning). Desired Qualifications Publications in health sciences and biomedical journals. Demonstrable interdisciplinary research experience. Deep understanding of machine
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. Target start date is January 2026. Appointment Start Date: January 2026 Group or Departmental Website: https://woods.stanford.edu/ (link is external) https://sustainability-accelerator.stanford.edu/ (link
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related to staff position within a Research Infrastructure? No Offer Description Description of the workplace Automatic Control is an exciting and broad subject, covering both deep mathematics and hands
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and development of perception stacks for autonomous mobile systems in general in any field Machine learning/deep learning experience applied to perception and any experience with deep Learning
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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks
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, interdisciplinary project. At the end of the project, you will have: a deep understanding of the hydrodynamic processes that control the dispersion of buoyant macroplastic items in the coastal zone; expertise in
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deep neural activity, using methods that combine physics (waves in complex media) and computational approaches. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR8552-SYLGIG-011
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) Experience in deep learning algorithms is a plus Ability to work in a highly international team and interdisciplinary project applicants are expected to have excellent language skills in English Opportunity