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, computational modelling (DFT, MD, etc.) Good publication record Good written and spoken communication skills Programming skills We seek a diverse and inclusive workforce and are committed to equality
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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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and Mathematical Sciences | NTU Singapore We are looking for a Research Fellow to study quantum materials via Machine Learning. The role will focus on develop Machine Learning technique to help DFT
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experience in DFT calculation on 2D heterostructures for optical applications, nanofabrication, or 2D optoelectronic devices fabrication and measurement. Applicants are invited to contact Prof. Yang Ming
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materials using DFT (VASP) and molecular dynamics with machine learning force fields is desired. • Experience and skills in scientific programming (e.g. Python or C/C++) and data analysis, including
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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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: Experience with developing efficient numerical algorithms and modifying electronic structure DFT or quantum chemistry software (e.g. Quantum Espresso, PySCF, GPAW), fluency with electronic structure theory and
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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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modeling and simulations (e.g., DFT, MD, AIMD) to provide molecular-level insights into polyamide interfacial polymerization, interfacial structure evolution, and transport mechanisms, in close integration
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, UV-vis, FL…etc.) is essential. The preferred candidate will have a deep understanding of the chemistry of the main-group elements. Experience with DFT or quantum-chemical calculations is also preferred