S³ 2025

Rockets are the only machines powerful enough to reach orbit, carrying satellites, astronauts, and scientific instruments beyond our atmosphere. In 2023 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.

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.