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Senior Research Engineer in DFT for Materials Science, AI for Science at Microsoft
Berlin, North Holland, Germany -
Full Time

Start Date

Immediate

Expiry Date

27 Feb, 26

Salary

0.0

Posted On

29 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

DFT, Materials Science, Deep Learning, Quantum Chemistry, Python, C++, Fortran, Computational Materials Science, Open-Source Software, Software Engineering, Model Evaluation, Data Generation, High-Performance Computing, Software Packages, Collaboration, Technical Communication

Industry

Software Development

Description
Implement and maintain evaluation pipelines for exchange correlation functionals for materials using software packages like VASP, CP2K, QuantumEspresso, FHI-aims, PySCF, or similar Work cross-functionally with deep learning and quantum chemistry researchers and engineers to build and maintain model evaluation and data generation pipelines for exchange-correlation functionals for materials. Prepare and maintain open-source releases and releases for beta testers Work cross-functionally with deep learning, quantum chemistry researchers and engineers to align model development strategies with high-performance integration into CPU and GPU-based DFT software frameworks for materials. Completed (or about to complete) PhD in physics, chemistry, computational sciences, mathematics, or a related area Experience with computational materials science, especially DFT and its limitations Development experience with DFT solid state software packages (VASP, QuantumEspresso, CP2K, FHI-aims, ) Proficiency in collaborative software engineering in Python and in C++ or Fortran Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds Experience with maintenance of open-source libraries or commercial software packages
Responsibilities
Implement and maintain evaluation pipelines for exchange correlation functionals for materials. Work cross-functionally with researchers and engineers to build and maintain model evaluation and data generation pipelines.