For any questions, do not hesitate to ask:
Dr. Michael Bussmann Tel.: +49 3581 37523 11,
+49 351 260 2616,
Dr. Attila Cangi
Mrs. Inken Köhler Tel.: +49 3581 37523 10,
Mrs. Weronika Mazur Tel.: +49 3581 37523 23,
+49 171 3635554
Place of work:
31 July 2020
The HZDR is committed to equal opportunity employment and we strongly encourage applications from qualified female candidates. We also carefully consider all applications from job candidates with severe disabilities.
Bautzner Landstraße 400
PhD Student (m/f/d) - Machine-Learning DFT Simulation Package for Electronic Structures under Extreme Conditions
A member of the Helmholtz Association of German Research Centers, the HZDR employs about 1,200 people. The Center's focus is on interdisciplinary research in the areas energy, health and matter.
The Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for dataintensive 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.
The Department on Matter under Extreme Conditions is looking for a PhD student (m/f/d) interested in developing a Machine-Learning DFT Simulation Package for Electronic Structures under Extreme Conditions. The position will be available from now. The employment contract is limited to three years.
The Scope of Your Job
Your project will contribute to the ambitious long-term goal of achieving a more accurate and consistent understanding of HED phenomena in the warm dense regime across multiple length and time scales. You are expected to develop a machine-learning (ML) DFT simulation package for calculating energies and atomic forces in configurations of atoms, at a scale and cost unattainable with direct DFT algorithms. You will implement a computational workflow that predicts the local density of states (LDOS) at each grid point in real space as a function of its local environment based on high-fidelity training data generated from DFT-MD. You will also investigate how the accuracy of the ML-DFT methodology varies with respect to the ML methods used (convolutional neural networks, sequence learning, and natural language processing) and the extent of the physics requirements captured. You will verify the effectiveness of the ML-DFT simulation package with calculations of the equation of state of aluminum and silica as surrogates exhibiting the typical challenges encountered with conventional first-principle methods. You will carry out your research in collaboration with our partners at international research institutions.
generate high-fidelity training data for materials under HED conditions (aluminum and silica) with DFT-MD
design and implement a computational machine-learning workflow to predict the LDOS, energies, and forces
implement and analyze several ML techniques and physics constraints in the ML-DFT workflow
compute the equations of state of aluminum and silica under ambient and HED conditions
publish your results in academic, peer-reviewed journals
present your results at scientific meetings
very good university degree (Master/ Diploma) in physics or in a related subject
a solid background in mathematics, physics, materials science, or in a related subject
excellent programming skills in languages such as Fortran, Python, or C/C++
experience in methods development or data generation with electronic structure codes (such as VASP, Quantum Espresso, Elk)
experience in data modeling with machine learning (such as Tensorflow, Pytorch)
strong motivation to work in a collaborative environment
excellent communication skills in English and in a professional context (presentation of research results at scientific meetings, colloquial discussions, writing of manuscripts)
a vibrant research community in an open, diverse, and international work environment
scientific excellence and high quality
broad national and international science networks
salary and social benefits in conformity with the provisions of the Collective Agreement TVöD-Bund (30 vacation days per year, company pension plan)
a good work/life balance for which we offer assistance in the shape of:
flexible working hours
in-house health management
Kindly submit your completed application (including cover letter, CV, diplomas/transcripts, etc.) only via our Online-application-system.