This workshop is part of the AWASES project (AI-Aware Pathways to Sustainable Semiconductor Process and Manufacturing Technologies) funded by Intel & Merck. Hosted by L3S/TIB, it brings together researchers across experiment, simulation, and data science to explore AI, FAIR modeling, and autonomous chemistry in semiconductor research.
The program features invited talks by leading researchers at the interface of artificial intelligence, materials science, and chemistry.
📌 Event Details
Dates: May 21–22, 2026
Start: 9:00 a.m. (May 21)
End: 12:00 p.m. (May 22)
Format: Onsite
👥 Open Community Call for Participation
We invite researchers, practitioners, and students to attend the workshop and engage with current developments in AI-driven research.
If you would like to attend without presenting a poster, please register via
👉 https://forms.gle/YpQ5cKe8Wzd3o1H2A
A registration fee of 50 EUR applies.
The workshop will take place on May 21–22, 2026, beginning at 9:00 a.m. on May 21 and concluding at noon on May 22. The program includes invited talks, poster sessions, and opportunities for discussion and networking.
As space is limited, registration may close once capacity is reached.
PhD Candidate | Eindhoven University, Netherlands
PhD Candidate | Leibniz University, Hannover
PhD Candidate | University of Warwick, UK
Eindhoven University, Netherlands
Eindhoven University, Netherlands
University of Warwick, UK
Leibniz University/L3S/TIB, Hannover
Leibniz University/L3S/TIB, Hannover
The AWASES program is co-sponsored by Intel Corporation and Merck KGaA, Darmstadt, Germany, supporting research at the intersection of semiconductor process innovation, data science, and artificial intelligence.
The AWASES project is a collaboration between Eindhoven University of Technology, the University of Warwick, and the Leibniz University.
The workshop is connected to broader initiatives developing open research infrastructure for data-driven science, including activities within the National Research Data Infrastructure (NFDI) in Germany.
These efforts promote FAIR data practices, reproducible workflows, and AI-ready scientific knowledge systems.
Contact us: sciknoworg [at] gmail.com