Postdoc: AI-Guided Sb2S3 Thin-Film Photovoltaics

DTU — Technical University of Denmark

Kongens Lyngby

Vor Ort

DKK 420.000 - 540.000

Vollzeit

14 Tage+

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

DTU Electro seeks a Postdoctoral Researcher to advance sustainable solar energy by developing Sb₂S₃ and Sb₂(S,Se)₃ thin films and exploring defect control through synthesis.

You will join a multidisciplinary team integrating hydrothermal synthesis, device fabrication, and AI-guided optimization to close the loop between experiments and machine-learning models for next-step experiments.

Qualifikationen

  • Strong academic background in physics, nanotechnology, materials science, electrical engineering or related field.
  • Hands-on experience in solar-cell fabrication and emerging PV technologies is essential.
  • Experience with hydrothermal thin-film synthesis or solar-cell device characterization is advantageous.

Aufgaben

  • Develop and optimize hydrothermal synthesis of Sb₂S₃ and Sb₂(S,Se)₃ thin films.
  • Investigate relationships between synthesis, crystal structure, composition and defect formation.
  • Fabricate and characterize thin-film solar cells to assess PV performance.
  • Collaborate with DTU Compute to implement AI-guided experimental optimization.
  • Participate in group teaching and supervise PhD/MSc/BSc student projects.
  • Disseminate research results through publications and conferences.

Kenntnisse

Hydrothermal synthesis
Thin-film photovoltaics
Solar-cell device fabrication
AI/ML collaboration
Defect engineering

Ausbildung

PhD degree

Tools

Machine learning tools

Jobbeschreibung

DTU Electro seeks a Postdoctoral Researcher to advance sustainable solar energy by developing Sb₂S₃ and Sb₂(S,Se)₃ thin films and exploring defect control through synthesis.

You will join a multidisciplinary team integrating hydrothermal synthesis, device fabrication, and AI-guided optimization to close the loop between experiments and machine-learning models for next-step experiments.

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