Position: Research Internship at CKL Project: Multiscale Machine Learning for Computational Materials Science 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: 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