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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
. Preferred Qualifications, Competencies, and Experience Programming skills (e.g., python, bash scripting, Fortran, C++ and CUDA), expertise in computational modeling (such as Deep Learning, Molecular Dynamics
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executed. In close collaboration with PhD researchers and project partners from TUM and ETH, you will contribute to the development of novel control and learning methods for aerial manipulators and multi
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large
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computer vision tools (e.g., MediaPipe, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection or analysis. Familiarity with deep learning frameworks (PyTorch
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, and artificial intelligence methods. Assess drought stress and identify physiological traits associated with drought tolerance using advanced imaging technologies. Develop machine learning and deep
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, and environmental data. The research will involve machine learning, human-centred experimentation, real-time comfort prediction, and the integration of intelligent climate control systems into vehicle
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more than 250 employees and is responsible for more than 1700 students. Working at CNAP CNAP offers postdoctoral fellowships for exploring neuroplasticity and pain. As a postdoc you will contribute to our
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
-scale neuroimaging datasets. Responsibilities for this position includes: * Developing deep-learning approaches for insight and analysis about mouse behavior from video * Machine Learning: use your
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental