AI for Fundamental Research

Keywords: machine learning, artificial intelligence, neural networks, particle physics, real-world AI applications, science communication
The successful AI & Particle Physics course returns to Discover—this time with an even stronger focus on artificial intelligence and neural networks! While we will still explore some of the fundamental mysteries of the universe, our primary goal is to understand how AI can help scientists solve problems in physics and beyond.

Machine learning has transformed industries, from medicine to finance to art—but did you know it’s also revolutionizing scientific research? AI is now a crucial tool for analyzing experimental data, recognizing patterns in particle detectors, and even simulating the behavior of fundamental particles. In fact, the 2024 Nobel Prize in Physics recognized the groundbreaking contributions of AI to science, highlighting just how essential neural networks have become in modern research.

In this course, you’ll build and train your own neural network, explore how machine learning techniques apply to physics, and experiment with real-world AI tools used in scientific research. Finally, you’ll also sharpen soft skills critical for any scientist, such as communicating complex discoveries to the public and media.

Class 1: Introduction to Machine Learning – What is AI and why does it matter?
Class 2: Neural Networks & Deep Learning – Understanding the core technology
Class 3: AI in Science – Using machine learning to solve problems in physics
Class 4: Hands-on Project – Train your own neural network to classify particles
Class 5: Communicating Science – How to present discoveries effectively

Requirements: Basics of high school physics and mathematics (recommended), laptop (optional), curiosity (essential)

Oliver Matonoha

Oliver, a native of Prague, earned his PhD in Particle Physics from Lund University in Sweden, where he studied the strangely behaving inner constituents of protons — quarks and gluons. His research focuses on analyzing vast amounts of high-energy particle collisions to uncover new insights about quarks and gluons. Oliver works within international scientific collaborations at CERN in Geneva, near the breathtaking Alps, and at Brookhaven National Laboratory in the United States. Beyond physics, Oliver has become increasingly engaged with the transformative role of artificial intelligence in scientific research. His work now extends to deep learning applications in particle physics, particularly in leveraging AI for complex data analysis of high-energy collisions. Passionate about education and knowledge accessibility, he enjoys teaching and mentoring students, bringing AI and physics closer to young minds through various educational and outreach initiatives. Outside of research, Oliver finds joy in electronic music, skiing, writing poetry, and learning how to DJ. He also nurtures a thriving collection of over 30 plants and enjoys trying out new recipes to share with friends.

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