20 combinatorial-optimization Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI
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: Investigating membrane fouling mechanisms and mitigation strategies in desalination and water treatment processes. Developing and optimizing functional membranes, including electrically conductive membranes
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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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, 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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, 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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of substitution models using large dataset, successful applicants must then have a PhD and demonstrated experience in discrete choice models, machine learning techniques, big data, and optimization
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. The candidate is expected to carry out cutting-edge research on the physical layer design and optimization of RHS-enabled or RIS-assisted MIMO communication systems using tools from information theory
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validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous computing where the focus will be to work on to the current efforts
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to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement
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publication record is encouraged and previous experience in areas such as Distributed control and optimization, Swarms, Autonomous Robots, Embedded systems development, Collaborative and adversarial control
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power (CSP) systems optimization, computational heat transfer and radiative transport using sophisticated numerical modeling and machine learning approaches for forward and inverse problems in radiation