Postdoc - Machine Learning for Materials Modeling

Postdoc - Machine Learning for Materials Modeling

HZDR - Helmholtz-Zentrum Dresden-Rossendorf

Görlitz, Germany

Scope of Your Job

In this role, you will contribute to ongoing research efforts in developing scalable machine learning models for applications in materials science. Your tasks include contributing to the development of the Materials Learning Algorithms software package and applying machine learning models beyond the scope of machine learning interatomic potentials. Target applications include heterogeneous materials and nanoscale electronics.

Your tasks

• Data Generation: Generate datasets using first-principles simulations software (density functional theory and related codes);
• Automated Workflows: Utilize automated workflows on high-performance computing systems for efficient data generation;
• Model Development: Develop transferable machine learning models and apply them to answer questions in materials science;
• Collaboration: Collaborate with peers, both within and beyond the research group, on related research topics;
• Mentoring: Supervise and mentor junior group members;
• International Engagement: Collaborate with our international partners;
• Dissemination: Present your scientific findings at academic venues and publish research in peer-reviewed journals.

Your profile

• Completed university studies (PhD) in the field of Physics, Computer science, Materials science, Chemistry, or a related field;
• Proficiency in programming languages (Python, C/C++, Julia);
• Background in machine learning methods;
• Experience in developing, training, and tuning machine learning models;
• Prior exposure to collaborative software development and version control systems (Git);
• Experience with electronic structure and molecular dynamics simulation codes (VASP, QuantumEspresso, CP2K, LAMMPS);
• Motivation to work collaboratively in a team-oriented environment;
• Excellent communication skills.

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