PhD - Medical science
(SS 2025)
Scientific Computing and Reproducible Data Analysis in Biomedicine
Lecturer
- Lukas Forer , PhD
Content
Dates
Monday, May 19, to Thursday, May 22, from 10:00 AM to 4:00 PM each day.
Content
Reproducible data analysis and scientific computing are essential for processing large and complex datasets across various fields of biomedical research. The aim of this lecture is to introduce students to scientific computing, high‑performance computing (HPC), and reproducible data analysis in biomedical research. We use genetic and biological datasets to learn how reproducible data analysis can be achieved using workflow managers, notebooks and computing clusters. Additionally, the lecture introduces open science and FAIR data principles and shows how state‑of‑the‑art analysis tools can be used and adapted. The lecture includes practical components that apply computational techniques to real‑world applications, inspiring and motivating students to integrate these methods and technologies into their own research
Aims
- Understand the importance of reproducible data analysis,open science and FAIR data principles in biomedical research.
- Understand the role of scientific computing and workflow managers in advancing biomedical research.
- Get a basic understanding of High‑Performance Computing and the resources available at the Medical University.
- Gain insight into real‑world applications through examples in genomics.
- Learn best practices for planning and structuring data analysis experiments.
- Develop the skills to design, build, and execute computational workflows.
- Learn how to apply these methods and technologies to own research questions