Research Internship at CKI - Multiscale Machine Learning for Computational Materials Science

Position: Research Internship at CKI

Project: Multiscale Machine Learning for Computational Materials Science

Scientific supervisor: Prof. Dr. Andrey Ustyuzhanin

Introduction to CKI:

The Constructor Knowledge Institute (CKI) 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: Multiscale Machine Learning Model for Predicting Magnetization Properties of Graphene Flakes.

The goal of this project is to design and train a multiscale machine learning (ML) model capable of predicting the magnetization properties of graphene flakes. The model will integrate two levels of data representations to account for both atomic-level and higher-level features of graphene flakes, providing a comprehensive approach to predicting magnetic behaviors efficiently.

Challenges / key research questions:

• Integrate atomic-level and higher-level data to create a multiscale model.
• Ensure the model can effectively predict complex magnetization behaviors.
• Handle high-dimensional feature spaces while maintaining model efficiency.
• Address the computational cost of training and prediction in multiscale models.
• Generalize across different graphene flakes and their magnetic properties.

Requirements:

• Python skills
• Experience with AI libraries
• Prompt engineering
• Math and data processing skills are plus