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conversational AI and multimodal interfaces, Bias detection and fairness-aware tools for ethical AI. Integrate machine learning models, visualization dashboards, and backend services. Contribute to data collection
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for ethical AI. Integrate machine learning models, visualization dashboards, and backend services. Contribute to data collection, testing, documentation, and dissemination of open-source resources
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processing or willingness to learn quickly. Publications, thesis work, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification
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of this project is to add support for automatic code optimization in Tiramisu. In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module
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IEEE Signal Processing Society’s journals and conferences. Strong background in communication theory, signal processing, machine learning, and optimization theory. Strong verbal and written skills in
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Junior Research Scientist in the Center for Quantum and Topological Systems (CQTS) – Dr. Hisham Sati
arises. Applicants must have a Bachelors in one of the following: Computer Science, Computer/Electrical/Communication Engineering, Mathematics, Physics. For consideration, applicants need to submit a cover
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capable of understanding, learning, and acting in complex, dynamic settings. The lab’s work lies at the intersection of computer vision, multimodal learning, and robotics, advancing next-generation embodied
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complementary data from other Mars missions to strengthen current models and provide comparative insights that enhance research conclusions from Hope observations. Develop Machine Learning methods and run
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AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials
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applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines