Postdoctoral Position - AI for Materials

Physical Science and Engineering Division

Organisation/Company KAUST Department Physical Science and Engineering Division Research Field Engineering " Materials engineering Chemistry " Computational chemistry Physics " Computational physics Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Country Saudi Arabia Application Deadline 1 Jan 2026 - 21:19 (Africa/Abidjan) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

About the Position

We are seeking a highly motivated postdoctoral researcher to join KAUST in the field of advanced materials design, with a focus on AI for Materials. The project will leverage machine learning and high-throughput first-principles calculations to accelerate materials discovery, working at the intersection of computational science, materials engineering, and artificial intelligence.

Main Research Directions includes:

Machine Learning Methods and Models

• Uncertainty quantification, active learning
• Generative modeling
• Dataset quality assessment (redundancy, similarity, diversity)

AI-Accelerated Modeling

• Automating atomistic modelling workflows
• ML methods for workflow orchestration
• Dataset curation and ML interatomic potential development

AI and Computational-Experimental Integration

• Transfer learning, multimodal learning
• Automated spectral data analysis
• LLM-based design

Candidate Profile

Candidates should have a background in materials science, chemistry, physics, or a related field and be comfortable with computational work (coding, using HPC, etc.). Experience in atomistic modeling (DFT, MD, MC) and machine learning is preferred but not required.

Where to apply

E-mail

Requirements

Research Field All Education Level PhD or equivalent

Additional Information Work Location(s)

Number of offers available 1 Company/Institute King Abdullah University of Science and Technology (KAUST) Country Saudi Arabia Geofield