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Senior Researcher

3 weeks ago

Singapore beBeeResearch Full time $120,000 - $150,000
Research Opportunities in AI-Accelerated Materials Discovery

The research fellow will join a dynamic group to advance the discovery of nanoporous materials using artificial intelligence and machine learning techniques.

This exciting opportunity is part of an ongoing effort to accelerate the development of sustainable technologies for energy and environmental applications.

Job Description:

The successful candidate will contribute to the design, development, and application of machine learning models to predict structural and electronic properties of nanoporous materials.

Key responsibilities include:

  • Developing and applying graph neural networks and deep generative models to predict material properties
  • Constructing and expanding curated databases of porous materials through text mining, graph theory, and computational chemistry outputs
  • Performing high-throughput computational screening of porous materials for gas adsorption, separation, and catalysis
  • Enhancing molecular simulations by coupling AI models with quantum chemical calculations
Requirements:

To be considered for this position, applicants must possess a PhD in Chemistry, Materials Science, Chemical Engineering, Physics, or a related field.

A strong background in nanoporous materials and structure-property relationships is essential, as is a proven research track record evidenced by publications in peer-reviewed journals.

Excellent communication skills and a strong interest in interdisciplinary collaboration are also required.

Skills and Qualifications:

The ideal candidate will have expertise in computational materials science, including density functional theory (DFT), molecular simulations, and other multi-scale modeling techniques.

Strong programming skills in Python, C/C++, and MATLAB, as well as experience with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn, are necessary.

Experience:

The successful candidate will have applied AI-driven methods to accelerate nanoporous materials discovery and designed predictive ML models for estimating partial atomic charges and screening candidate materials for gas separation.

Collaboration across computational and experimental teams to validate predictions and deepen understanding of structure-property relationships is also expected.

We are looking for candidates who can tell us about their relevant skills and qualifications in these areas:

  • TensorFlow
  • Materials Science
  • Physics
  • Chemistry
  • PyTorch
  • Computational Chemistry
  • Matlab
  • Python
  • Programming
  • Chemical Engineering