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, climate, and human health. Examples of current active projects include: Developing optimization models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
Geotechnical Engineering, Civil Engineering, or a related field, and should demonstrate strong expertise in at least two of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian
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algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems
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with ML libraries, multimodal AI models, etc. Exposure to security and privacy research work Excellent written and verbal communication skills Track record of research with publications in top-tier
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-Doctoral Associate to work on a fascinating project focused on the geomechanical modeling for energy applications. The position will be directly supervised by Professor Mostafa Mobasher and will involve
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. We are seeking a Postdoctoral Researcher to join the team and make significant contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model
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language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems This position can be filled
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models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations and/or experimental
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Responsibilities The PDA will conduct research to design and develop optical wireless communication systems. This involves the development of mathematical models for signal transmission/reception, derivation
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design