TIME TO INNOVATE
Live Streamed Seminar
DOE and Bayesian Optimization in Practice
Date: 19 Nov 2026
Time: 11:00–12:00 CET | 10:00–11:00 GMT
Duration: 1 hour
Location: Online (Zoom)
Registration: Free
Understand complex systems with fewer, more informative experiments
When time, materials, and experimental capacity are limited, every experiment needs to deliver useful insight.
Changing one factor at a time can feel like the most straightforward approach. But when several factors influence a result, it may take more testing to understand which factors matter, how they interact, and where to focus next.
Design of experiments (DOE) and Bayesian optimization offer complementary approaches to this challenge. DOE can help you build a deeper understanding of your system, while Bayesian optimization can help you use what you have learned to identify promising experimental conditions efficiently.
Learn how to:
- Identify the factors and interactions driving your results.
- Gain more useful insight from fewer experiments.
- Know when to use DOE, Bayesian optimization, or both.
- Use what you learn to focus future experiments on the most promising conditions.
See how these approaches can be applied across research, development, and manufacturing to make experimentation more informative and efficient.
Who should attend?
Scientists, engineers, researchers, and process development professionals who want to better understand complex systems, reduce unnecessary testing, and make more effective use of experimental data.
New to DOE or Bayesian optimization?
No prior experience is required. The webinar focuses on practical applications, real-world examples, and accessible demonstrations, making it suitable for any skill level.
Can I ask questions during the session?
Yes! The webinar concludes with a live Q&A where you can ask about specific challenges.
Is there a cost to attend?
No. This webinar is free.
Can’t join us live? Register anyway and we will send the recording after the event.