26 wireless-sensor-network positions at King Abdullah University of Science and Technology
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We are seeking motivated researchers with expertise in large-area 2D materials growth for sensor and transistor applications. The focus is on developing wafer-scale 2D materials using CVD, PLD
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structures for composite infrastructures: Wireless surface gauges and integrated sensors, SHM/NDT/Inline/Online. The Composites Lab develops and authenticates techniques to achieve better designs of composite
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macroscopic response: Toughing mechanism using surface on involume spatial variations. Structural health monitoring (SHM) and smart structures for composite infrastructures: Wireless surface gauges and
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variations. Structural health monitoring (SHM) and smart structures for composite infrastructures: Wireless surface gauges and integrated sensors, SHM/NDT/Inline/Online. The Composites Lab develops and
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state-of-the-art genomic technologies. We ask fundamental questions on how cells operate as molecular machines with special reference to the dynamic gene regulatory networks governing cellular identity
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. This includes facies analysis, environmental research, diagenesis, pore-network analysis and stratigraphy. Additional ideas will also be considered. A multitude of data is already available including shallow
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Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone, CAN: Creative Adversarial Networks, International Conference on Computational Creativity(ICCC), 2017 Theme B continual learning (selected papers
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of bioinformaticians, computer scientists, biotechnologists, biologists, and biochemists. The successful candidate will also enjoy an environment aimed to facilitate progress in the research career: networking, student
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-class students, faculty, and researchers from over 100 nationalities. The result is a network of talent that enables radically dierent perspectives. With holistic support for creative minds and unmatched
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and biostatistics, with expertise in mathematical and statistical modeling techniques including agent-based models, compartmental models, and network analysis. Previous experience in investigating