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Field
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-generation permanent magnets using a powerful combination of high-throughput computation, machine learning, rapid synthesis and efficient characterization. This position requires expertise in synthesizing and
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computational chemistry techniques, including molecular dynamics, quantum mechanical simulations, and machine learning. Proficiency in programming languages and computational software’s. Strong motivation and
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application aspects of the following areas, and will offer internationally competitive salary packages for the successful candidate. Topics of Interests Post-Quantum Cryptography Multi-Party Computation Zero
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precision in a broad range of applications from enhanced microscopy, spectroscopy, and communications to quantum computing and sensing. Students—undergraduate, Master’s and PhD—are involved in all aspects
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application to quantum theory and information science. Other application areas of interest include robust parameter estimation and performance bounds under model misspecification, integrated sensing and
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in experimental physics and superconducting device development, with a focus on advancing multipixel single-photon camera technology and multiplexed readout for quantum information science applications
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information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum
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Duties: Perform and analyze ReaxFF reactive molecular dynamics simulations on 2D-material growth Connect the results from these simulations to data science methods and Computational Fluid Dynamics Perform
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Postdoctoral Research Positions in Colloidal Quantum Dots for Infrared Photodetectors Our lab has made significant advances in III–V colloidal quantum dots (CQDs) and extended-SWIR PbS materials
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Postdoctoral Research Positions in Colloidal Quantum Dots for Infrared Photodetectors The FuN Lab at KAUST has made significant advances in III–V colloidal quantum dots (CQDs) and extended-SWIR