170 adiabatic-quantum-computers Fellowship positions at Nanyang Technological University
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topological quantum materials. Our group aims to discover new topological quantum phases of matter, explore their physics and apply them to address challenges in electronics, photonics, quantum computing
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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photonics, quantum optics, or quantum information science. At least 2 years of relevant research experience with hands-on experimental work in integrated photonics, optical or quantum photonic systems
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We are seeking a Research Fellow to lead research on advanced Rydberg atom–based quantum sensing platforms at CQT, NTU. The role involves experimental work on electromagnetic signature detection
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research on halide perovskite quantum dots (PQDs) for optoelectronic and display applications. The role focuses on the design, synthesis, stabilization, and integration of PQDs into polymer-based color
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descriptions to support ongoing projects. Job Requirements: PhD or MSc/MEng in Physics, Computer Science, or related disciplines with a focus on quantum physics or quantum information, or digital twins and
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topological quantum materials. Our group aims to discover new topological quantum phases of matter, explore their physics and apply them to address challenges in electronics, photonics, quantum computing
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) who is highly skilled in and deeply passionate about computational electromagnetism and mathematical physics/engineering. The SRF should have strong background in computational methods for solving
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fundamental understanding and practical applications of quantum correlations and information processing. We invite applications for a research position in quantum information science. The successful candidate
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superlattices (twistronics). The role will focus on developing and applying theoretical models and computational quantum chemistry and machine learning methods to uncover novel properties and phenomena in low