Research Internship at CKL â Foundation Models for Quantum Material Design
Research Internship at CKL â Foundation Models for Quantum Material Design
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Job Description
Position: Research Internship at CKL
Project: Foundation Models for Quantum Material Design
Scientific supervisor:Prof. Dr. Andrey Ustyuzhanin
Introduction to CKL:
The Constructor Knowledge Labs (CKL) intends to set the worldwide standard for research into Computer Science AI and Machine Learning Robotics and Neuroscience operating in strong contact with industry and:
- Leveraging CS technologies to address challenges in various fields delivering innovative solutions tailored to industry needs.
- Providing research opportunities mentorship and involvement in collaborative projects to young researchers and PhDs.
- Encouraging interdisciplinary research by fostering collaboration between diverse fields emphasizing the integration of theoretical research with practical application
Project: Foundation Models for Quantum Material Design
This project aims to develop foundation models (large pre-trained AI models) tailored for the design of quantum materials. These models will be trained to understand the complex high-dimensional relationships between material properties quantum mechanical behaviors and experimental data allowing for the efficient design of novel materials with specific quantum characteristics. The project will explore how to apply AI models particularly large-scale neural networks to predict material properties such as electronic structure magnetism superconductivity and more based on quantum mechanical principles. It will also focus on integrating data from experiments and simulations to optimize the design process reducing the need for exhaustive trial-and-error experimentation.
Challenges / key research questions:
- Capture the intricate non-linear relationships governing quantum materials.
- Integrate quantum mechanical principles with materials science and experimental data.
- Ensure the foundation models generalize across various material systems.
- Balance model complexity with computational efficiency for large-scale predictions.
- Achieve high accuracy in predicting properties that are yet to be experimentally observed
Requirements:
- Python skills
- Experience with AI libraries
- Prompt engineering
- Math and data processing skills are plus
Required Experience:
Intern
Employment Type
Intern
