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Field
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developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation of results. Deliver ORNL’s mission by aligning
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model. Developing and coding the appropriate algorithms and methods that implement the novel concepts and model. Gathering experimental or observational data to test hypotheses or highlight the strength
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Requisition Id 15448 Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large
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of the multiple diversities – linguistic, cultural, religious, biological – that characterise societies and urban environments, and will contribute to the development of multi-level sustainable policies for human
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scale and resolution. This ambitious project spans multiple institutes including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and the Human-Centered Artificial Intelligence Institute, bringing
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for Pollinator Monitoring: Train and optimise deep learning models for pollinator detection and classification using annotated image datasets. Post-processing object tracking algorithms will be incorporated
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and in-house developed software to predict structures of interacting proteins and in collaboration with the Steinegger lab, developed highly efficient AI-based algorithms to compute similarity between
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park, in a dynamic ecosystem that brings together academics and companies of all sizes. The Signal team of the i3S Laboratory (https://i3s.univ-cotedazur.fr/signal ), aims to develop advanced, innovative
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algorithms, ideally in the healthcare or mental health domain Desirable criteria Track record of successful research grant applications, or attempts to obtain grant funding. Previous development of ML-based
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-house database of experimental real-world data enabling large-scale validation of developed algorithms. Wind turbine drivetrains are critical components, and their failures can lead to significant