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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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robotic middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic
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-edge machine learning methods with empirical insights from the educational arm of the project. A central technical challenge guides this position: How can an LLM-based AI social agent be designed, fine
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National Aeronautics and Space Administration (NASA) | Merritt Island, Florida | United States | about 10 hours ago
or machine learning applied to plant traits. Candidates with strong quantitative skills, interest in interdisciplinary collaboration, and motivation to explore crop performance in novel environments may find
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well as exploring the application of research findings to advanced 3D models such as organoids and 3D bioprinted tissues Learning about high-content, automated phenotypic drug screening pipelines against high
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spectral based sensing, including Ultrasound and Hyperspectral Imaging (HSI), Artificial Intelligence (AI) and Tiny Machine Learning (TinyML). Duties As a Postdoctoral researcher you are expected to perform
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across tissues, over time and upon perturbation for translational benefit, leveraging tissue profiling through multiomic technologies in conjunction with cutting-edge computational and machine learning
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partner who focuses on the underlying machine learning models, and you will help coordinate the joint work across the two teams. The position offers a strong publication trajectory at leading HCI venues
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the