Workshop: Design of Experiment

Designing experiments efficiently is a key challenge in research, particularly when time, materials, or measurement capacity are limited. Design of Experiments (DoE) offers a structured approach to planning experiments more effectively. This workshop will cover the basics of DoE and demonstrate how Bayesian Optimization can further improve experimental design. In a hands-on session, we will explore BayBE (https://github.com/emdgroup/baybe) - an open-source Python library for Bayesian Optimization developed by Merck KGaA. Through examples from practical applications, you will see how BayBE supports scientists in navigating complex experimental spaces, accelerating discovery, and improving outcomes even when data is scarce. 

A technical documentation can be found at https://emdgroup.github.io/baybe/stable/index.html.

Registration

Register for the workshop during the registration process for the PhD seminar. Number of participants is limited to 25. An additional fee is taken for the workshop.

Schedule

25.09.2026 (full day)

Start is currently planned at 9 a.m. (may be shifted to 10 a.m).

End is currently planned at 3 p.m. latest.

Prerequisites

Will be published around 4 weeks before the workshop.