GaN Computational Materials Postdoc - Growth & AI Modeling

Institute of High Pressure Physics, Polish Academy of Sciences; Polska; On-site; PLN 167,000 - 223,000; Full time

Job summary

Institute of High Pressure Physics, Polish Academy of Sciences in Poland seeks a Junior Postdoctoral Researcher for computational materials science focused on GaN and related III‑nitride systems. The role combines theory, simulations, and experimental collaboration to model growth, defects and properties.

The 40‑hour work week and a contract until end 2029 support advanced research in different WP areas, with ML‑driven process optimization and AI tools to link parameters with material quality.

Qualifications

• PhD in Physics, Materials Science, Computational Materials Science, Electrical Engineering or related field.
• Publications in peer‑reviewed journals, aligned with career stage.
• Strong background in solid state physics, quantum mechanics and materials science.

Responsibilities

• Carry out theoretical, computational and experimental studies on GaN and related III‑nitride materials.
• Model and optimize ammonothermal GaN crystal growth and related processes.
• Develop digital twin concepts by integrating physics models with data and ML.
• Collaborate with experimental teams across WP1–WP3 and publish results.

Skills

Python programming

DFT methods

HPC experience

Machine learning

Data analysis

English proficiency

Education

PhD in Physics

PhD in Materials Science

PhD in Computational Materials Science

PhD in Electrical Engineering

Tools

VASP

Quantum ESPRESSO

SIESTA

Job description

Institute of High Pressure Physics, Polish Academy of Sciences in Poland seeks a Junior Postdoctoral Researcher for computational materials science focused on GaN and related III‑nitride systems. The role combines theory, simulations, and experimental collaboration to model growth, defects and properties.

The 40‑hour work week and a contract until end 2029 support advanced research in different WP areas, with ML‑driven process optimization and AI tools to link parameters with material quality.