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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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with the application of Algorithms, Artificial intelligence (AI), and Machine Learning (ML) methods to accelerate material discovery and design, optimizing manufacturing processes in nanoscience
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imaging datasets and advanced machine learning approaches to identify novel imaging markers of mental health disorders and cognitive function; 2) developing robust MRI-based acquisition, image
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, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services/records/compensation
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-centered learning as well as how students from backgrounds traditionally considered “outside” the academy experience and engage in STEM learning and career training. The postdoctoral associate will
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will also use focussed ion beam milling scanning electron microscopy (FIB-SEM) to prepare infected cells for in situ cryo-ET. The resulting tomographic data will be analysed by machine-learning assisted
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incorporate clinical, lifestyle, and nutritional factors to build predictive models through advanced bioinformatics and machine learning. By identifying molecular signatures that distinguish responders from non
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–based, web-enabled decision-support tool for farmers, using machine learning (random forest) predictive models. Working closely with the PhD student and PIs to ensure rigorous model development