Do you want to advance your academic expertise in STEM, network in an international and interdisciplinary science community, and create long-lasting memories while enjoying the Finnish summer? The Jyväskylä Summer School on 4 – 15 August offers a unique academic and cultural journey that gives you the opportunity to challenge yourself and develop your skills in your field of interest!
For over three decades, the Jyväskylä Summer School has gathered students from all over the world to deepen their expertise on Science, Technology, Engineering, and Mathematics subjects and expand their professional and academic networks. Organised by the Faculty of Mathematics and Science and the Faculty of Information Technology at the University of Jyväskylä, the Jyväskylä Summer School is one of the largest and oldest summer schools in Finland.
The 34th Jyväskylä Summer School will be organised on 4 – 15 August 2025. The application period is now open until 30 April.
This year’s Summer School courses fall mostly under the themes of Quantum Science and Probability Theory and Advanced Approaches for Secure and Intelligent Technologies. All courses are taught in English by distinguished researchers from different parts of the world. Participation in all Summer School courses is free of charge.
Inverse problems-related courses at the Jyväskylä Summer School:
Mathematics of X-ray Computed Tomography
Lecturer: Tatiana Bubba (University of Ferrara, Italy)
Contents: In this course we learn about the mathematics of X-ray computed tomography, what is the regularization theory of inverse problems, and how some signal processing ideas are relevant in the context of inverse problems.
Prerequisites: Basics of linear algebra, numerical and functional analysis.
Introduction to Uncertainty Quantification for Inverse Problems
Lecturer: Babak Maboudi Afkham (University of Oulu)
Contents: In this course, we will explore how to formulate inverse problems within a Bayesian framework. This involves representing both noise and unknowns using probability distributions. We will then define the solution to the inverse problem as the conditional probability distribution of the unknown given the measurements, commonly known as the posterior distribution. Finally, we will examine how to interpret the posterior to quantify the uncertainty in our predictions and reconstructions.
Prerequisites: Basics of numerical and computational skills, coding in Python is mandatory; basic knowledge of probability theory and statistics.
In addition to intensive academic work, the Jyväskylä Summer School also offers an extensive programme of extracurricular activities such as Finnish sauna, get-togethers, picnics, and cultural events for you to socialise with fellow participants, have fun, and enjoy the summer in the beautiful Jyväskylä region to the fullest!
For further information, full course programme, and application instructions, please visit: www.jyu.fi/jss
Photo: Visit Jyväskylä Region / Keijo Penttinen. Cropped from the original.
