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Fellow in Autonomous Systems and Control to design and implement efficient, performance‑guaranteed distributed control approaches, leveraging cutting‑edge learning algorithms and AI strategies
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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, Dental Materials, Dental Technology or a related discipline, with research experience in Non-linear Finite Element Analysis, CAD for dental prostheses, AI algorithm development using python / PyTorch, and
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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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Description The Quantum Information team at UMass Amherst is involved with modeling and optimization of quantum hardware, as well as development of new modeling methods and algorithms, in collaboration with
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are in particular targeting development of data-driven high-performance computing techniques for unbiased discovery of generative models & theory and algorithms for network inference with special reference
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algorithms. The research focuses on wind energy applications, creating a compelling sustainability narrative: developing more efficient computational methods to optimize wind farm performance, which in turn
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logistics. Develop and implement control algorithms for distributed multi-agent system operations and spacecraft-based robotic manipulation. Perform research on spacecraft dynamics and control, including
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fundamentally more energy-efficient Computational Fluid Dynamics algorithms. The research focuses on wind energy applications, creating a compelling sustainability narrative: developing more efficient
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences