474 algorithm-development-"Multiple"-"Prof"-"Prof"-"SUNY"-"Simons-Foundation" positions
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Field
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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automated recovery algorithms, improving system resilience. Research Areas for Master’s and PhD Students AI-Enhanced Resource Forecasting and Optimization: Research Focus: Developing and testing ML algorithms
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to programming, data structures, algorithms, application development, and software engineering. - Utilize inclusive pedagogies and course design to effectively promote student learning. - Engage in curriculum
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technology and develop human resources. The AI Computing Team explores the design and realization method of advanced machine learning systems by working across multiple layers, including circuits, devices
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, focusing on intelligent sensor tasking and the automated identification and characterization of space objects in Earth orbits and cislunar environment using optical data. Contribute to the development
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algorithms, and machine learning to solve complex aerospace engineering challenges. Developed and implemented AI-driven solutions for autonomous lunar and asteroid landings, as well as cislunar operations
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Scalable Inference: Develop new algorithms for scalable uncertainty quantification (UQ) and Bayesian inference and apply them to challenging simulation problems. The goal is to produce robust, validated
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the development of image/signal processing algorithms from a multidisciplinary approach, to include multiple sensor modalities. These multidisciplinary research opportunities incorporate theoretical and
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understanding of KB development Working with specific Robotics applications in multiple domains Proficiency with various state-of-the-art Computer Vision models Project 2. - PhD Position in Sustainable AI
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teams. Identify, research, and develop algorithms to exploit information in various sensor systems Analyze and develop models of signal processing outputs to support system understanding Present results