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the design and development of multiple projects using state-of-the-art AI models, algorithms, statistical models, and other programs designed to improve the public sector. Work with large untapped data
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healthcare application needs to analyze sensitive patient data across distributed nodes. Researchers and students can explore privacy-preserving algorithms and technologies like federated learning and zero
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Project (Next-Generation Solvers for Complex Microwave Engineering Problems). This project aims to design and develop physics-guided, data-driven algorithms that can accurately solve complex microwave
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Overview Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and
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suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
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unified autonomous vehicle scene representation. Our project consists of theoretical fundamental, adversarial attack/defence algorithms, and robustness-oriented understanding methods to enhance
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algorithms. Strong programming skills in Python and/or R. Experience in analyzing both quantitative and qualitative research data. Experience in statistical consulting, dashboard development, and data
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interplanetary travel. Autonomous Systems: Develop AI algorithms, robotics, and autonomous software to enhance spacecraft operations and exploration. Implement machine learning techniques for improved autonomy and
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exploration. Data Science and Analysis: Develop algorithms and tools for data processing, modeling, simulations, and analytics to support scientific research and mission planning. Spacecraft Operations: Oversee
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for aircraft and spacecraft. Develop and enhance avionics components and systems, including integrating avionics with guidance, navigation, and control (GN&C) algorithms. Focus on advancing autonomous flight