Postdoc in ML-Driven Double-Perovskite Oxynitrides

Syddansk Universitet; Odense; On-site; DKK 420,000 - 540,000; Full time

Job summary

Mads Clausen Institute at SDU seeks a Postdoctoral Research Associate to advance machine-learning driven discovery of novel double-perovskite oxynitrides. Work spans DFT, high-throughput screening, data science, and collaboration with NanoSYD within CatMatX. Expect supervision of MSc/PhD students and active publication activity.

The role emphasizes integration of computational predictions with experimental synthesis and materials evaluation for energy applications.

Qualifications

• PhD in computational materials chemistry or closely related field.
• Strong background in first-principles DFT calculations and solid-state chemistry.
• Experience with ML-driven materials discovery and high-throughput workflows.

Responsibilities

• Perform DFT calculations and high-throughput screening for double-perovskite oxynitrides.
• Develop materials-informatics and ML approaches for accelerated discovery.
• Collaborate with experimental teams and supervise graduate students.

Skills

DFT calculations

Machine learning

High-throughput screening

Materials informatics

Python programming

English proficiency

Education

PhD in Computational Materials Chemistry

Tools

VASP

Quantum ESPRESSO

GPAW

CASTEP

Job description

Mads Clausen Institute at SDU seeks a Postdoctoral Research Associate to advance machine-learning driven discovery of novel double-perovskite oxynitrides. Work spans DFT, high-throughput screening, data science, and collaboration with NanoSYD within CatMatX. Expect supervision of MSc/PhD students and active publication activity.

The role emphasizes integration of computational predictions with experimental synthesis and materials evaluation for energy applications.