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Ph.D. position at the Center for Advanced System ... (No replies)
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Ph.D. Student: Machine-learning Mesoscale Simulation Framework for HED Phenomena
The Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. We combine innovative methods from mathematics, theoretical systems research, simulations, data science, and computer science to provide solutions for a range of disciplines – materials science under ambient and extreme conditions, earth system research, systems biology, and autonomous vehicles.
CASUS was jointly founded in August 2019 by the Helmholtz-Zentrum Dresden-Rossendorf, the Helmholtz Centre for Environmental Research, the Max Planck Institute of Molecular Cell Biology and Genetics, the Technical University of Dresden and the University of Wroclaw. CASUS is located in the heart of Görlitz at the border between Germany and Poland. The CASUS start-up phase is hosted by the Helmholtz-Zentrum Dresden-Rossendorf and is financed by the Federal Ministry of Education and Research and the Saxon State Ministry of Science and Art.
The Department on Matter under Extreme Conditions is looking for a Ph.D. student interested in developing a Machine-learning Mesoscale Simulation Framework for HED Phenomena. Consideration of candidates will begin immediately and will continue until the position is filled.
The Scope of Your Job
Your project will contribute to the ambitious long-term goal of achieving a more accurate and consistent understanding of high energy density (HED) phenomena in the warm dense regime across multiple length and time scales.
You are expected to develop a molecular dynamics (MD) simulation framework for predicting magnetic, electronic, and phononic degrees of freedom in HED matter at the mesoscale. The core of the simulation framework is the LAMMPS code which relies on interatomic potentials (IAPs). Based on high-fidelity training data generated using DFT-MD you will generate quantum-accurate IAPs by employing the Spectral Neighbor Analysis Potential (SNAP) methodology. You will explore various state-of-the-art machine learning models to capture the complexities of the electronic structure under HED conditions. Furthermore, you will verify the effectiveness of the simulation framework by calculating the kinetics of magneto-structural phase transitions in iron as a surrogate for many complex HED processes and phenomena. You will carry out your research in collaboration with our partners at international research institutions.
Submit your application (including a one-page cover letter, CV, academic degrees, transcripts, etc.) online on the HZDR application portal. Further details can be found there under Job-Id: 75/2020 (995).