Postdoctoral Fellow in Energy & AI

United Arab Emirates University, Department of Family Medicine; Abu Dhabi; On-site; AED 331,000 - 477,000; Full time

Job summary

The Postdoctoral Research Fellow will contribute to an interdisciplinary project focused on improving the thermal stability and performance of perovskite solar cells using nano-enhanced phase change materials and AI-driven predictive modeling.

The role involves designing and fabricating PSC devices, synthesizing PCM/Ne-PCM materials, integrating systems for thermal management, and performing thermal, electrical, and durability testing.

Qualifications

• 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.

Responsibilities

• Design and fabricate PSC devices.
• Synthesize and characterize PCM/Ne-PCM materials.
• Integrate systems for thermal management under harsh conditions.
• Conduct experimental testing (thermal, electrical, durability).
• Analyze thermophysical properties and generate datasets.
• Develop and apply machine learning models to predict performance.
• Prepare publications and assist in mentoring students.

Skills

Thermal systems

Solar energy

Phase change materials

Nanomaterials synthesis

Characterization techniques

Machine learning / AI tools

Publications

Perovskite PV knowledge

Education

PhD required

Tools

Python

MATLAB

TensorFlow

Job description

Job Description

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.

Minimum Qualification

• 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 Qualification

• 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.

11/11/2026