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Physics, or a related discipline. Experience with recognized computational chemistry software (e.g., Gaussian, Dalton, TurboMole) is highly desirable. Experience in Time-Dependent Density Functional Theory
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important - Prior experience with density functional theory or machine learning is desirable - Proficiency in the Python programming language is important, as well as Fortran - Strong written and oral
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of materials science, combining ab initio density-functional theory (DFT) calculations with novel ML methods. You will develop ML-assisted computational screening methods, building highly accurate models
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will involve both computational predictions and experimental validation. The project will combine density functional theory calculations with machine learning and molecular dynamics simulations
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understanding of neutron scattering and magnetism; Experience with density functional theory; Experience in statistics and programming. • Manual skills and rigorous attention to detail are essential in this work
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enzyme active site density functional theory (DFT) ‘cluster’ or QM/MM models. The Hough group at Diamond develops methodologies for ambient temperature, time resolved macromolecular crystallography and
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functional theory for electrolyte systems Predicting interfacial tension of electrolyte systems Benchmarking electrolyte thermodynamic modelling approaches We are looking for academic excellence, and we expect
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methods to accelerate the discovery of better catalysts that use less platinum and have improved long-term stability. By combining large-scale density functional theory with machine-learned interatomic
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phonon eigenvalues and transport properties using computational methods (density-functional theory, molecular dynamics, and finite-element simulation). It predicts the intrinsic phononic features
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Fellow will join the Particle, Astroparticle and Cosmology Theory Group at the University of Stavanger, which conducts research in the following areas: QCD at high density and temperature Gravitational