ESem Medick-Krakau

Venue

• every week: Friday 4th block (13:00-14:30), ASB/328
• every second week: Wednesday 6th block (16:40-18:10), SE2/201

House keeping details

• This course will be given entirely in English.
• Please use the enrollment to be part of the lecture.
• The course comprises, lectures, labs and a capstone project.
• The lecture comprises 3 SWS and 4.5 ECTS credits if this is needed.
• If you like to pass the lecture, you have to complete a capstone project.

Video Conference Details if required

Join Zoom Meeting
hzdr-de.zoom-x.de/j/89839487704?pwd=QXp3MWc4WVNTRFdBeldhRWVBU0RwUT09

Meeting ID: 898 3948 7704
Passcode: 767523

Important Dates

capstone project handout to students: Dec 1, 2025
poster submission for print: Feb 2, 2026
poster presentation: Feb 6, 2026

Lecturers

Peter Steinbach: Peter received his PhD in Particle Physics in 2012 from TU Dresden (Germany) for an experimental study of LHC data using the ATLAS experiment to reduce background contributions to Higgs Particle searches. He continued to industry (Scionics Computer Innovation GmbH) as a HPC support and software engineer helping scientists push the limits of their applications in a service oriented group at the Max Planck Institute for Molecular Biology and Genetics. In this role, he became increasingly exposed to Deep Learning applications for vision applications in biology. In 2019, he started to lead a group of Helmholtz AI consultants at Helmholtz-Zentrum Dresden-Rossendorf. The team's has a mandate to help scientists from the research field matter within the Helmholtz association to use machine learning in experiment and theory. Peter is an avid open-source enthusiast, reproducibility evangelist and volunteer lecturer at TU Dresden.

Sebastian Starke: Sebastian graduated from Otto-von-Guericke University Magdeburg (Germany) as a trained mathematician and statistician. He received his PhD in biostatistics in 2024 from TU Dresden (Germany) where he was part of the 'Modeling and Biostatistics in Radiation Oncology' group at Oncoray led by Prof. Steffen Löck. During this time, he worked in the context of personalized radiotherapy, focussing on the task of improving outcome predictions of cancer patients by combining statistical survival analysis with medical image-based deep learning approaches. Since 2020, Sebastian has also been part of the Helmholtz AI consultant team led by Peter Steinbach at HZDR, where he supports Helmholtz scientists in their (mostly imaging-related) machine learning endeavours like denoising, segmentation, regression or uncertainty estimation.

Target Audience

• learners registered in a TUD masters program (bachelor students are allowed if they consider the level of the course appropriate)
• learners from any natural science domain (physics preferred, biology and chemistry encouraged)
• learners from related domains such as computer science, engineering, digital humanities, etc or any graduate studies (e.g. PhD students) of these domains are also welcome

Prerequisites

• fundamentals of python programming (basics of numpy, virtual environments and jupyter notebooks): see here for a self-study material to brush up your python
• (suggested) fundamentals of matrix-vector algebra
• (suggested) fundamentals of statistics