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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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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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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
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the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning modelling. This exciting project involves the application of innovative
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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Internal Number: 6808723 Sr. Machine Learning Engineer About the Opportunity JOB SUMMARY The Sr Machine Learning (ML) Engineer applies expertise in deploying and scaling AI pipelines across at least one