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to conduct research in the areas of safety-critical control theory and machine learning. The role will focus on combining new theory or method in nonlinear system control and state-of-the-art machine learning
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background in earthquake monitoring. The successful candidate will lead and contribute to developing a machine-learning powered earthquake monitoring and early warning system. The role involves both
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activity should focus on conducting research on machine learning methods, image processing, and advanced data analysis. Furthermore, participation in research projects, active grant applications, and
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 22 days ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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protein foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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involve developing an approach that uses Knowledge Organization (KO) metadata and ontologies to optimize parallel processing and scheduling policies (via Kubernetes) for Machine Learning tasks. The fellow
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publication record in sensing systems, mobile health, or HCI. Experience with signal processing, machine learning, or embedded systems is a plus. Carnegie Mellon University is an equal opportunity employer. It
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University of North Georgia is a University System of Georgia leadership institution and is The Military College of Georgia. More details on the UNG Mission, Values, Vision, and Culture can be found at https
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 10 days ago
). Experience in machine learning methods, including data preprocessing, model selection, and performance evaluation. Familiarity with programming languages commonly used in these domains (e.g., Python, MATLAB