S3 2025
July 20 - July 31, 2025
Projects
The bulk of your time at the School will be spent working on a research project. You may indicate your preference regarding the project in your application, but teams will be assigned upon arrival to the School, once you get to meet the project leaders in person.
You can read more about the projects available at this camp below. The details of some projects will be announced soon.
This IS rocket science: an introduction to rocket motion
Rockets are the only machines powerful enough to reach orbit, carrying satellites, astronauts, and scientific instruments beyond our atmosphere. In 2024 alone, there were more than 180 rocket launches, highlighting the growing interest of both research and industry in reaching space. Placing spacecraft in orbit is essential not only for exploring the universe but also for studying our planet and learning how to protect it. I know rocket science might seem incredibly hard—and don’t get me wrong, it is—but are you curious about how rockets actually work? How do they generate enough thrust to defy gravity, and how can we control their flight so precisely?
In this project, we'll dive into the physics and engineering that make rocket launches possible. You will explore multiple branches of physics— dynamics, thermodynamics, and aerodynamics—while applying an engineer's mindset to solve real-world challenges. You will discover how rocket engines produce thrust, why stability is critical, and how each component influences performance. Through hands-on experiments, you will test how factors such as size, shape, and balance affect a rocket’s flight. Finally, we will evaluate how well engineering models describe reality by launching a small rocket and comparing the real-life results with mathematical predictions. By the end of this course, you'll gain not only a deeper understanding of rocket science but also insight into complex physics derivations and how engineers apply them to real-world problems.
Francesco Bondini
TU Delft, The Netherlands
Francesco is a first-year MSc student in Aerospace Engineering at TU Delft. His main interests in the field are Astrodynamics, Attitude Control and Rocket Motion. He is also a member of the university rocketry team, currently working on the rocket trajectory simulation and stability analysis. His main passion outside of science is music, he loves playing the piano, the guitar and the bass guitar. Having a good time with friends and meeting new people is also something he can't live without.
Fairness in Machine Learning – Can Computers make Fair Decisions?
We live in a time where Machine Learning is getting more and more important, with AI models being used for an increasing number of tasks, such as sorting through job applications or identifying academic fraud. Therefore, it is important to make sure that computers make good decisions that do not unfairly discriminate anyone. For example, nobody wants their university application denied because of their race or their gender. But what exactly does it mean to make a fair decision? How can we teach that to an algorithm? And how do algorithms “decide” or “learn” something, anyway?
Those are the questions we will attempt to answer in this project! To that end, we will look at different definitions of fairness, how to express them so that a computer can understand them and evaluate both how accurate and how fair or unfair some machine learning models are. You will learn how to evaluate algorithms and we will discuss how different evaluation methods are in conflict with each other. We will also consider what kind of role datasets and potential data bias play for fairness in machine learning. And we will be working on how to make machine learning models more fair. You will learn the basic theory behind a few standard machine learning models and train and test models of your own. Our work will focus mainly on classification models – which sort data samples into pre-defined classes – and we will mainly use smaller models rather than deep neural networks. We will work with the programming language Python, but no previous experience in coding is required.
Kathrin Lammers
Bielefeld University, Germany
Kathrin is a first-year PhD student in the Machine Learning Group at the University of Bielefeld, where she also did her undergrad and master’s degree in computer science. Her current research focuses on stream learning and fairness. Kathrin participated in several regional summer school programs during her time at school. Initially intimidated by coding, which she never learned at school, she decided on computer science only during university open days and has not regretted that decision. She also enjoys English literature, crafts and going on long walks through the woods.
Physics demo project: Something very interesting
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Matheus Azevedo Silva Pessôa
Physics Department, McGill University, Canada
Matheus is a physics PhD student from McGill University studying the physics of DNA molecules for genomics applications. For his MSc, he studied radioastronomy and Cosmology. During undergrad, participating in the International Physicists’ Tournament (IPT) guaranteed experience in multiple fields of physics at a time, always with an experimentally driven approach. Building a setup for sonoluminescence is one of his personal dreams.
Earthquakes yikes: How to do something?
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Chem. Project Leader
Faculty of Chemistry, University of Chemistry, Buthan
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Wet lab that we really need - real-life desperate bacteria
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Biology P. Leader
Department of Biochemistry, University of Hambridge
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Workshops
Coming soon...
Lectures
Coming soon...