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team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed
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or willingness to learn quickly. Publications, thesis work, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification and model
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 days ago
modules leveraging deep learning for classical problems such as segmentation and 3D object tracking interfacing machine learning code and the robot using ROS2 contributing to the creation of datasets
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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Learning, or a related field. A Master’s degree is preferred. ASR/TTS Expertise Experience in training and fine-tuning Automatic Speech Recognition (ASR) or Text-to-Speech (TTS) models, preferably in
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for the Advancement of Surgery) initiative. The Research Associate will be responsible for developing machine learning algorithms and creating predictive models. The ideal candidate must demonstrate a robust background
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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) Neuromodulation approaches (TMS, tDCS, TUS) Neurogenetics Computational modelling (machine learning, reinforcement learning) Our research bridges scales (local circuits to global networks) and species (humans, mice
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Developer for the Crop and Science Department at Oregon State University (OSU). We’re looking for a motivated student to develop computer vision models that detect and classify important agricultural elements
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teaching faculty to teach an undergraduate course, Machines that Create, an introductory yet comprehensive overview on Generative AI and Foundation Models, covering the methods and techniques driving modern