Postdoc: AI-Driven Sb2S3 Thin-Film PV & Solar Cells

Danmarks Tekniske Universitet

Denmark

On-site

DKK 480,000 - 600,000

Full time

5 days ago
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Benefits offered by this job

PhD supervision
International collaboration

Job summary

DTU Electro invites a postdoctoral researcher to advance sustainable solar energy by combining thin-film synthesis with AI-driven optimization, focusing on Sb2S3 and Sb2(S,Se)3, and studying how synthesis conditions affect defects and device performance.

You will develop hydrothermal synthesis methods, fabricate and characterize solar cells, and collaborate with DTU Compute to guide experiments with machine-learning models.

Qualifications

  • PhD required in physics, nanotechnology, materials science, electrical engineering, or related field.
  • Hands-on experience in solar-cell fabrication is essential.
  • Experience with hydrothermal thin-film synthesis or solar-cell device characterization is advantageous.

Responsibilities

  • Develop and optimize hydrothermal synthesis of Sb₂S₃ and Sb₂(S,Se)₃ thin films.
  • Investigate relationships between synthesis, structure, composition, and defect formation.
  • Fabricate and characterize thin-film solar cells and evaluate performance.
  • Collaborate with DTU Compute to implement AI-guided optimization.
  • Supervise PhD and MSc/BSc projects and contribute to publications.
  • Disseminate results at international conferences.

Skills

Research experience
Solar cell fabrication
Hydrothermal synthesis
AI-guided experimentation
Supervision

Education

PhD degree in a relevant field

Tools

Characterization equipment

Job description

DTU Electro invites a postdoctoral researcher to advance sustainable solar energy by combining thin-film synthesis with AI-driven optimization, focusing on Sb2S3 and Sb2(S,Se)3, and studying how synthesis conditions affect defects and device performance.

You will develop hydrothermal synthesis methods, fabricate and characterize solar cells, and collaborate with DTU Compute to guide experiments with machine-learning models.

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