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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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are hiring a postdoctoral researcher to work on rethinking extraction methods at one of OCP Group’s production sites. The project will rely on Operations Research and Machine Learning approaches. The objective
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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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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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field. Strong background in machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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. This unique position combines advanced finite element modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an
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computing and machine learning. The research will emphasize both theoretical advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more