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Monash/Bayreuth Double PhD Degree Opportunity - Accelerating organic semiconductor discovery with generative AI, molecular dynamics and DFT Job No.: 698040 Location: Clayton (home) and Bayreuth
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 2 months ago
and storage. Your tasks # Performing DFT, MD, Wannier, Phonon calculations # Collaborating with peers including experimental project partners # Publishing results in academic and peer-reviewed journals
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-functional-theory (DFT) reference calculations; sample lithiation and phase transitions using Grand-Canonical Monte Carlo coupled to molecular dynamics, including biased sampling when needed; reconstruct
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learning for materials discovery or quantitative image analysis, DFT calculations for catalyst design, experiences with MATLAB / Python / AutoCAD / COMSOL • Organic synthesis, polymer chemistry, synthesis
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of spectroscopic measurements. Investigate underlying receptor-analyte interactions with the aid of in silico calculations, including density functional theory (DFT) simulations. Apply machine learning approaches
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electrocatalysis to contribute to the externally funded GETCO2 projects and collaborate with experimental researchers. Your key responsibilities will be to: perform first-principles/DFT studies of electrocatalytic
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Density Functional Theory (DFT) calculations to model hydrogen-tramp element co-segregation at grain boundaries and phase boundaries Perform DFT to obtain atomistic insights into how tramp elements interact
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compositionally complex circular steels. As a PhD researcher, you will: Perform Density Functional Theory (DFT) calculations to model hydrogen-tramp element co-segregation at grain boundaries and phase boundaries
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functional theory (DFT) calculations of NMR parameters, powder X-ray diffraction, and other complementary analytical techniques. The postdoctoral researcher will be responsible for planning and conducting
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error estimate, and a full record of how it was computed. Build and scale MLIP/MD/DFT workflows (e.g., atomate2, MACE/CHGNet/UMA-class potentials) to hundreds of compositions on HPC. Validate predictions