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Field
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-scale epidemiological datasets using statistical and machine learning methods Conduct systematic literature reviews and meta-analyses on disease dynamics topics Develop and validate predictive models
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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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, or a related technical field. ● Strong programming skills in Python, Java, etc. ● Expertise in machine learning, neural networks, and deep learning ● Excellent writing and communication skills ● Highly
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 4 hours ago
, machine learning and statistical methods to elevate impacts research. There is also opportunity to work at the nexus of water and agriculture, as well as in risk management for suburban landscapes. Location
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-doctoral fellow position in the areas of machine learning, statistics, and data science. Chapman University is a nationally ranked institution offering traditional undergraduate and graduate programs in
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the possibility of yearly renewal subject to funding availability. Key Responsibilities • Conduct and lead research in 3D computer vision, deep learning, and AI for digital twin generation, publish findings in top
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Michael Bronstein, AITHYRA Scientific Director AI and Honorary Professor of the Technical University of Vienna in collaboration with Ismail Ilkan Ceylan, expert in graph machine learning, invites
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scholar to conduct research at the intersection of robotics, controls, and machine learning. The successful candidate will help shape next-generation approaches for autonomous multi-robot systems
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performs MRI research and development of advanced multiparametric methods for the evaluation of primary and metastatic brain tumors. Recent work incorporates machine learning methods to advance