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Field
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multi-agent autonomous systems and related technologies. This will include development of distributed monitoring algorithms enabling agents in a multi-agent swarm to autonomously locate other agents in
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimizing PIC algorithms for modern heterogeneous
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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes
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Encryption, Privacy-Preserving Algorithms Computer Science & Engineering: Quantum Algorithms, Blockchain, High-Performance Computing, Algorithm Implementation *About Prof. Jintai Ding Prof. Jintai Ding is a
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guarantee for various machine learning methods; and designing fast and efficient algorithms and analyzing the convergence performance of those algorithms; co-authors scientific publications and reports
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and deploying UAV simulation environments using AirSim or its derivatives. Designing and implementing navigation, planning, and obstacle avoidance algorithms using ROS2. Conducting simulation studies
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arrays are individually controlled, interrogated, and even entangled with other atoms. We seek to leverage the many-fold technical QIS advances to develop new algorithms for optical clocks as sensors
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motivated Post-Doctoral Associate to join our team with a strong background in robot control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance
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reconstruction, 3D modelling software (Autodesk Revit, Dynamo), including open-source frameworks and algorithms for structural and M&E BIM reconstruction. 3. Experience in Python programming, machine learning