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experience at the time of application; and (b) be familiar with theory of condensed matter, DFT, EPW, tight binding methods, ab initio modeling of electron-phonon mediated superconductivity and the
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computational materials science techniques (DFT, MD, machine learning force fields) with data-driven approaches. Design and implement high-throughput experimental workflows for thermal conductivity and phonon
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in the Department of Materials Science and Engineering at the National University of Singapore under the direction of Dr. Peichen Zhong. This role involves using DFT, molecular dynamics, and machine
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=s4o2noYAAAAJ&hl=en . Basic Qualifications Required Qualifications: Ph.D. in Chemistry, Physics, Materials Science, Chemical Engineering or a related field. Experience with plane wave-based DFT codes required
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computational simulations in the area of density functional theory (DFT). The role will focus on understanding and predicting the properties of novel functional materials, including surfaces and interfaces
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data (e.g. DFT, MP2) and experimental data into AI-driven pipelines for structure–property learning and reactivity prediction. Applying AI models to real-world problems in virtual screening, ligand
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computational materials science techniques (DFT, MD, machine learning force field modelling) with data-driven approaches. Work with team to design and implement high-throughput experimental workflows for rapid
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include density functional theory (DFT) and higher level methods to accurately screen new systems based on their optoelectronic and vibrational properties. The role holder will develop and apply
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quantum chemistry (DFT) or theoretical spectroscopy are encouraged to apply. Two selected review papers: J. Vicha et al. Chem. Rev. 2020, 120, 7065, doi: 10.1021/acs.chemrev.9b00785 J. Novotny et al. Acc
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include density functional theory (DFT) and higher level methods to accurately screen new systems based on their optoelectronic and vibrational properties. The role holder will develop and apply