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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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systems”, project number PN-IV-PCB-RO-MD-2024-0515.. The Experienced Researcher will carry out advanced theoretical DFT calculations and scientific research activities within the REDOXCOB project
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behaviour and electrically tunable interfaces in 2D heterostructures. Methods include density functional theory (DFT), atomistic simulation, high-performance computing, and machine-learning-assisted materials
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University of Girona (UdG) - Institute of Computational Chemistry and Catalysis (IQCC) | Spain | 2 months ago
, preferably in Python. Additional skills and experience considered positively:Experience in quantum mechanical calculations (e.g. DFT) Experience in molecular dynamics simulations Familiarity with Linux/Unix
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(Prof. Patrick Holland, Yale) and the LCC team. The consortium will employ a variety of approaches, including organic and inorganic synthesis, kinetic studies, mass spectrometry, DFT calculations, and
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science. Electronic structure calculations (e.g. DFT or tight-binding methods), or thermodynamic modelling (e.g. statistical mechanics, MD or CALPHAD). Scientific data analysis. Interdisciplinary research
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interest in one or more of the following areas is considered an advantage: Machine learning, deep learning, natural language processing or data science. Electronic structure calculations (e.g. DFT or tight
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for molecular magnetic materials lags behind these experimental breakthroughs. DFT fails to capture strong correlation, while wave function-based methods are computationally prohibitive for strongly-correlated
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made significant progress in this direction by merging machine learning interatomic potentials (MLIPs) trained on density functional theory (DFT) data, and enhanced sampling techniques to reach the
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highly challenging task. The project uses Machine Learning (ML), in combination with DFT and state-of-the-art Boltzmann transport methods, to predict, accelerate, and scale the computation of electronic