Postdoctoral Associate - Generative AI Materials Research

New York University; Al Ruways Industrial City; On-site; AED 200,880 - 312,480; Full time

Benefits offered by this job

Competitive compensation package

Housing and educational subsidies

Opportunities for international conferences

Professional growth in research

Job summary

New York University in Abu Dhabi is seeking a Postdoctoral Associate to lead generative AI materials research projects. The ideal candidate will have a PhD and strong expertise in AI, machine learning, and materials science.

This role involves creating advanced models for materials discovery, publishing research, collaborating on interdisciplinary projects, and supervising students. A competitive salary and benefits package, including housing and educational subsidies, are provided.

Qualifications

• PhD within the first five years after completion.
• Strong expertise in AI and machine learning methods for scientific research.
• Experience in AI‑driven materials discovery or generative AI.

Responsibilities

• Lead independent projects on generative AI for materials discovery.
• Build machine learning models for structure–property relationships.
• Collaborate on multidisciplinary research initiatives.

Skills

Python programming

PyTorch

TensorFlow

Machine learning and deep learning model development

Generative AI systems

Materials informatics

Structure–property relationship modeling

Scientific computing and data analysis

Technical writing and publication development

Academic mentoring and laboratory coordination

Education

PhD in Materials Science, Chemistry, Physics, Engineering or Computer Science

Job description

Position Summary

Postdoctoral Associate – Generative AI Materials Research. Location: Abu Dhabi, United Arab Emirates. Industry: Higher Education.

Job Type: Full-time. Experience: PhD with up to 5 years post-PhD experience. Salary: 18,000–28,000 AED per month.

Key Responsibilities

• Lead independent projects focused on generative AI for materials discovery and optimization.
• Build advanced machine learning models to establish structure–property relationships in materials science.
• Apply AI methods to study materials under extreme conditions and improve performance prediction.
• Collaborate with the Smart Materials Lab and the Center for Smart Engineering Materials on multidisciplinary research initiatives.
• Coordinate closely with experimental researchers to validate computational findings and support materials applications.
• Publish high‑quality scientific papers in peer‑reviewed journals and present findings at leading international conferences.
• Support scientific workshops and contribute to cross‑disciplinary academic collaborations.
• Assist in supervising undergraduate students and PhD researchers involved in laboratory and computational projects.
• Participate actively in strategic research development across the Smart Materials Lab.

Ideal Profile

• PhD in Materials Science, Chemistry, Physics, Engineering, Computer Science, or a closely related discipline.
• Within the first five years after PhD completion.
• Strong expertise in AI and machine learning methods for scientific research.
• Proven experience in AI‑driven materials discovery or generative AI related to materials applications.
• Excellent academic publication record with strong peer‑reviewed journal contributions.
• Ability to work independently while maintaining productive collaboration across multidisciplinary teams.

Skills Set

• Python programming.
• PyTorch.
• TensorFlow.
• Machine learning and deep learning model development.
• Generative AI systems.
• Materials informatics.
• Structure–property relationship modeling.
• Scientific computing and data analysis.
• Technical writing and publication development.
• Academic mentoring and laboratory coordination.

Benefits

• Competitive compensation package.
• Housing and educational subsidies.
• Opportunities for international conferences, workshops, and global academic exposure.
• Strong professional growth within a high‑impact scientific research environment.