67 postdoc-in-thermal-network-of-the-physical-building Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI
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Description The newly established NYUAD Wireless Research Center at New York University Abu Dhabi, seeks to recruit a Post-Doctoral Associate (PDA) who will conduct research on the physical layer
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, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must
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Description The newly established NYUAD Wireless Research Center at New York University Abu Dhabi, seeks to recruit a Post-Doctoral Associate (PDA) to engage in cutting-edge research on the physical
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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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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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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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initiatives spanning metabolic, cardiovascular, and neurologic diseases. This position offers an exciting opportunity to make a meaningful impact on next-generation surgical technologies, bridging the gap
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, Neuroscience, or a related field. A strong background in functional neuroimaging with experience in decoding and/or encoding models is required. Candidates with experience with recurrent neural networks will be
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aims to identify biomarkers in the eye and brain that explain vision loss, building on our previously-developed method linking clinical, neural and behavioral data (Allen et al., 2018; Miller et al
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(e.g., transportation networks, manufacturing systems, and truck routing). Assessing the relevance of the intake fraction (i.e., exposure efficiency) of major emission sources as a critical metric for