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. The group will be contributing to the Physics Modeling (MC software, MC validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous
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the areas of Artificial Intelligence (AI) for materials science, with an emphasis on structure-property-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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processes. Developing and optimizing functional membranes, including electrically conductive membranes, for use in desalination, energy generation, and electrochemical separations. Responsibilities: Conduct
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Responsibilities Develop, test, and optimize AI-driven pipelines for 3D reconstruction and rendering. Implement and evaluate neural rendering techniques using real-world digital heritage datasets. Collaborate with
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Circuits, Robust and Efficient Mapping of Quantum Algorithms on Quantum Machines, Quantum Noise-Aware Optimizations for QML, QML Security, Error Correction for Quantum Computing, Secure Quantum-Classical
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associate will lead collaborative efforts in advancing research focusing on the intersection of infrastructure, climate, and human health. Examples of current active projects include: Developing optimization
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code optimizations that are hard to apply otherwise. Because of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different
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candidate will work under the supervision of Professor Raed Hashaikeh in the Mechanical Engineering department. This project focuses on the development and optimization of conductive membranes
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, cardiovascular, and neurologic diseases. These projects entail computational modelling, device design and manufacturing, optimization of chemical, mechanical, and electrical characteristics, and preclinical