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computational design and wet-lab validation to establish predictive structure–property relationships. Perform quantum chemical calculations (e.g., DFT/TD-DFT) to interpret electronic structure and excited-state
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Theory (DFT) calculations using established codes (e.g., VASP, FHI-aims). Demonstrated experience with traditional methods for modeling atomic site disorder, such as special quasi-random structures (SQS
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coupled effects of size exclusion, electrostatic interactions, hydration phenomena, and interfacial reaction kinetics. • Apply atomistic modeling and simulations (e.g., DFT, MD, AIMD) to provide molecular
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a Research Fellow to contribute to a project focused on focused on data-driven discovery of atomic catalysts. Key Responsibilities: Theoretical predictions using DFT and machine learning, and
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skills is required: protein-ligand docking, DFT of organometallic complexes, machine learning or AI applied to chemistry or drug discovery, self-driving labs. Desired Qualifications* Experience in wet lab
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Functional Theory (DFT), machine-learned force fields (MLFF), graph neural networks (GNNs), or large language models (LLMs). Extensive Knowledge In: • First-principles atomistic simulations with packages
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apply approximation methods for interpolating electronic/phononic/thermodynamic properties of disordered crystal materials. Design and implement high-throughput density functional theory (DFT) workflows
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microkinetic modelling and computational chemistry (e.g. DFT) applied to catalysis Experience in organizing and maintaining a research laboratory (equipment maintenance, inventory, coordination of shared
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reactors/autoclaves Experience with borrowing‑hydrogen (hydrogen autotransfer) reactions Familiarity with microkinetic modelling and computational chemistry (e.g. DFT) applied to catalysis Experience in