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, developing and numerically solving diffusion reaction equations, parameter estimation, machine learning, and sensitivity analysis, with an emphasis in building open-source technologies that benefit the entire
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of the chemicals on droplet stability. You will be responsible for the following: Implementing AI imaging to analyze high-speed droplet movies. Relating the results to the HLD principle and performing its parameter
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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the necessary interface between the computer and the neural network of the human retina in vivo – without introducing additional modifications to our organisms. Such an interface could, in turn, be developed
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infrastructure monitoring, as well as connected autonomous vehicles Integrating multi-modal sensor data with physics-based models Developing robust and adaptive methods for real-time parameter and state estimation
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that can directly take raw, high-dimensional data from experiments or observatories and rapidly infer theory parameters, such as Higgs boson properties at the Large Hadron Collider or neutron star properties
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Dependent on Qualifications/Experience Proposed Start Date 02/01/2025 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching
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application to the European mission of a Digital Twin Earth. ML research directions will include physics-aware machine learning, reasoning, uncertainty estimation, Explainable AI, Sparse Labels and