Postdoctoral Fellow in Energy & AI - Mechanical & Aerospace Eng.

The Postdoctoral Research Fellow will contribute to an interdisciplinary research project focused on enhancing the thermal stability and performance of perovskite solar cells (PSCs) using nano-enhanced phase change materials (Ne-PCMs) and AI-driven predictive modeling. The role involves designing and fabricating PSC devices, synthesizing and characterizing advanced PCM/Ne-PCM materials, and integrating these systems for thermal management under harsh environmental conditions. The fellow will conduct experimental testing (thermal, electrical, and durability), analyze thermophysical properties, and generate high-quality datasets. In parallel, they will develop and apply machine learning models (e.g., neural networks, random forest, optimization algorithms) to predict system performance and optimize material and design parameters. The position also includes preparing publications, supporting project reporting, collaborating with the research team, and assisting in mentoring students, thereby contributing to the successful execution and dissemination of the project outcomes Grade: 19.

• PhD in Mechanical Engineering, Materials Science, Energy Engineering, Nanotechnology, or related fields. • Strong background in thermal systems, solar energy, or phase change materials. • Experience with nanomaterials synthesis and characterization techniques. • Knowledge of perovskite solar cells or photovoltaic systems is highly desirable. • Proficiency in machine learning / AI tools (Python, MATLAB, TensorFlow, etc.). • Demonstrated record of peer-reviewed publications. Preferred qualifications: • Experience in integrating experimental and computational research. • Familiarity with thermal modeling, heat transfer, and energy systems. • Ability to work in multidisciplinary teams (materials + AI + energy). • Strong scientific writing and communication skills.

Design and fabricate perovskite solar cell (PSC) devices; synthesize and characterize advanced PCM/Ne-PCM materials; integrate systems for thermal management under harsh environmental conditions; conduct experimental testing (thermal, electrical, durability); analyze thermophysical properties and generate high-quality datasets; develop and apply machine learning models (e.g., neural networks, random forest, optimization algorithms) to predict system performance and optimize material and design parameters; prepare publications; support project reporting; collaborate with the research team; assist in mentoring students.