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reclamation pilot-scale and lab-scale systems. Conduct membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning-based
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. Excellent experience in membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning based modelling. Relevant research
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details of 2-3 references to laura.cantini@pasteur.fr For more information : https://research.pasteur.fr/en/team/machine-learning-for-integrative-genomics/
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the project: Develop, train, and optimise deep learning models for wildlife species identification, classification, and segmentation using real-world datasets. Design and implement software modules to integrate
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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 | about 10 hours 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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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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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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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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) 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