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learning theory to join the research team of Prof. Muhammad Umar B. Niazi. The position focuses on the design and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems
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agreement. The successful candidate is expected to take a leading role in the development of the group tasks and help in supervising PhD students. Applicants must have a PhD in experimental high energy
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models and data science ideas. Applicants must have received a PhD in engineering, computer science, urban science, or a related field. Experience in transportation, in particular related to urban science
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infrastructure design, management, and policymaking. Investigating the residual sources of PM2.5 exposure from transportation systems following electric vehicle (EV) adoption, including exposure due to emissions
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strategies. Design, fabricate, and characterize functional membranes (e.g., conductive, anti-fouling, electrochemical systems). Operate and evaluate membrane systems (RO, NF, UF) at lab and pilot scales. Apply
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infrastructure design, management, and policymaking. Investigating the residual sources of PM2.5 exposure from transportation systems following electric vehicle (EV) adoption, including exposure due to emissions
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applicant will lead an innovative project focused on the development of surface-patterned membranes with tailored surface features designed to enhance the performance of microfiltration (MF), ultrafiltration
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soon receive a PhD in political science or a related discipline. The appointment will begin September 1, 2026, subject to final budget approval and is available for up to three years. The post-doctoral
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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
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to state-of-the-art research facilities in the Water Research Center (WRC) at NYUAD. Responsibilities: Conduct research on the design, fabrication, and characterization of electrically conductive and