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Field
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
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for molecular magnetic materials lags behind these experimental breakthroughs. DFT fails to capture strong correlation, while wave function-based methods are computationally prohibitive for strongly-correlated
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field. Strong computational chemistry background in atomistic simulations, electronic-structure theory, DFT, structure-property relationships, and interpretation of simulation results. Hands-on experience
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materials, leveraging informatics-driven approaches (machine learning, high-throughput ab initio, data-driven discovery) alongside advanced computational methods (e.g., DFT+DMFT, GW, quantum embedding
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well as with theoretical researchers specializing in atomistic simulation, density functional theory (DFT), and ab initio molecular dynamics (AIMD). The successful candidate will also engage with collaborators
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Materials Science (CMAT) is looking for: Doctoral researcher (PhD student) in area-selective atomic layer deposition (AS-ALD) simulations Are you interested in discovering and understanding chemical
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, Physics, Materials Science, or a related field Three or more years of postdoctoral experience Strong background in first-principles density functional theory calculations, particularly simulations of light
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highly challenging task. The project uses Machine Learning (ML), in combination with DFT and state-of-the-art Boltzmann transport methods, to predict, accelerate, and scale the computation of electronic
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Density Functional Theory (DFT)—frequently fail to balance the necessary accuracy with the required computational scale. Our group is developing a high-performance computational framework to bridge this gap
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(DFT, Molecular Dynamics Simulations, Docking, etc.). Proficient in oral and written English. Documented skills in academic writing. Ability to plan research work and critically assess and discuss