PhD Student in Electronic-Structure Machine Learning for Materials
Tasks
- Benchmark predictive performance for advanced properties
- Build automated reproducible AiiDA workflows
- Develop open source scientific software and workflows
- Develop transferable electronic structure machine learning models
- Explore transferable foundation models for materials
- Generate and curate electronic structure datasets
- Integrate machine learning frameworks with electronic structure codes
- Investigate model design and training strategies
- Validate electron phonon coupling predictions
Perks/Benefits
- N/A
Skills/Tech-stack
AiiDA | Benchmarking | Data Curation | Electronic structure | Electronic structure codes | Electronic structure simulations | Electronic structure theory | Machine Learning | Model Training | Predictive Analytics | Python | Quantum Mechanics | Scientific Computing
Education
Roles
Related jobs
-
PhD Student*in Modeling of water storage in the landscape for demand-oriented multi-purpose use CHF 85K-128KData Analysis | Geostatistics | Hydrological modeling | Hydrology | PythonMid-level Full TimeDübendorf, ZH, Switzerland5d ago
-
PhD Student Modeling of water storage in the landscape for demand-oriented multi-purpose use (m/f/d) CHF 48K-55KGeostatistics | Hydrological modeling | Python | R | Series analysisEntry-level Full TimeDübendorf, ZH, Switzerland5d ago
-
Computational modeling | Density Functional Theory | Scientific ComputingEntry-level Full TimeVilligen PSI, AG, Switzerland6d ago