Position: Research Internship at CKI Project: Foundation Models for Quantum Material Design 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: Cybersecurity threat research In this role, you will support research on threats, threat actors' tactics, techniques, and procedures (TTPs), and their infrastructure and operations. You will assist in discovering new approaches to collecting threat intelligence at scale, leveraging machine learning (ML) and AI to extract valuable insights for defenders. Your responsibilities will include contributing to the development of methods for identifying and analyzing digital evidence and indicators of compromise (IoCs) used by threat actors. You will also help research and build automated, scalable analysis pipelines and participate in implementing honeypots to track evolving attack trends. This internship offers hands-on experience in cutting-edge cyber threat research, exposure to advanced security tools, and the opportunity to collaborate with experienced researchers in a dynamic and innovative environment. Challenges / key research questions: How can machine learning improve the large-scale collection and analysis of threat intelligence, and what are the key challenges in automating this process? What novel methods can be developed to identify and analyze digital evidence and IoCs associated with threat actors more efficiently and accurately? What insights can honeypots provide on evolving attacker TTPs, and how can they be optimized to capture high-value threat intelligence? How do threat actors adapt their infrastructure and operations to evade detection, and what countermeasures can be developed to track them more effectively? Requirements: Python and C skills Linux skills Experience with AI libraries Experience with malware analysis and reverse engineering considered a plus