Postdoc: Reinforcement Learning for Materials Discovery

KAUST; Thuwal; On-site; SAR 260,000 - 340,000; Full time

Job summary

KAUST invites applications for three postdoctoral positions in the Computer, Electrical and Mathematical Science and Engineering Division, located in Thuwal, Saudi Arabia. The research focuses on reinforcement learning for materials discovery and direct air capture (DAC).

Join a diverse international team, publish at top conferences, and contribute to climate and sustainability goals. Candidates should have a PhD and a strong ML/DL background.

Qualifications

• PhD in a relevant field with strong focus on deep learning.
• Publications in top-tier journals and conferences.
• Proficiency in Python and DL frameworks such as TensorFlow or PyTorch.
• Experience in Reinforcement Learning or Evolutionary Algorithms.
• Background in Deep Learning for Chemistry is desirable.

Responsibilities

• Collaborate with international researchers and industry partners.
• Design, implement, and validate advanced reinforcement learning models.
• Use RL and evolutionary algorithms to discover new chemical materials.
• Publish and present findings at high-impact conferences.
• Engage in a supportive environment that fosters growth.

Skills

Deep learning

Reinforcement Learning

Publications

Education

PhD in Computer Science / Chemical Engineering / Materials Science

Tools

Python

TensorFlow

PyTorch

Job description

KAUST invites applications for three postdoctoral positions in the Computer, Electrical and Mathematical Science and Engineering Division, located in Thuwal, Saudi Arabia. The research focuses on reinforcement learning for materials discovery and direct air capture (DAC).

Join a diverse international team, publish at top conferences, and contribute to climate and sustainability goals. Candidates should have a PhD and a strong ML/DL background.