The 4th School on Data Science and Machine Learning invites ambitious researchers ready to harness the transformative power of advanced artificial intelligence. Building on our successful four-year legacy, this year’s program features state-of-the-art topics reflecting the rapid evolution of AI in 2025. Machine Learning is revolutionizing every sector of society — from breakthrough medical diagnostics to intelligent systems supporting vulnerable populations to innovative public safety solutions. These advancements aren’t just technological achievements; they are catalysts for new public policies and social frameworks. In this context, equipping researchers with advanced ML knowledge is crucial for the field’s continued development and responsible application.

Target Audience and Learning Approach
Our program is tailored for advanced PhD candidates finalizing research, early-career postdoctoral researchers, professionals seeking to integrate cutting-edge AI into their work, and researchers from diverse backgrounds looking to apply AI to their disciplines. We have designed a balanced learning approach with morning theoretical sessions paired with afternoon hands-on practical exercises that bridge theory and application.

What Makes This School Unique
Our forward-looking curriculum covers foundation models, advanced generative AI, efficient scaling, and frontier applications. We proudly feature leading Brazilian AI researchers alongside international pioneers, creating a rich environment for knowledge exchange. Participants will not just learn algorithms — they will develop critical thinking around AI applications, ethical considerations, and domain-specific implementations.

Networking and Peer Learning
To foster collaboration and peer learning, all participants are required to bring a research poster to be presented during coffee breaks and lunch intervals. This is an opportunity to showcase your work, receive feedback, and engage in meaningful discussions with fellow participants and instructors. The poster should include a concise description of your current research. If machine learning is already part of your work, the poster should explain how it is being used and, if available, present preliminary or final results. These sessions are designed to spark new collaborations and provide insights that can directly benefit your research.

Recommended background

• Early-career researchers (e.g., PhD students, postdocs, or final-year Master’s students)
• Fields: natural sciences, engineering, computer science, mathematics, social sciences, or humanities with interest in AI/ML
• Basic programming knowledge in Python
• Familiarity basic statistics
• No prior machine learning experience required, but interest and motivation are essential
• Proficiency in English (school will be held in English)

There is no registration fee and limited funds are available for travel and local expenses.