PhD students gained insights into Machine Learning and Lab Automation
At this year’s spring block course of the IMPRS-EPPC doctoral program, the PhD students received a hands-on training in the fundamentals of machine learning and lab automation. The workshops took place at the Fritz Haber Institute from March 23 to 26, 2026.
Researchers typically generate large amounts of digital data. The preparation, analysis, and archiving of research data can be significantly simplified using machine learning methods. To equip the doctoral students at the International Max Planck Research School for Elementary Processes in Physical Chemistry (IMPRS-EPPC) with the necessary tools during their training, the school dedicated this year’s spring block course entirely to the topic of "Machine Learning & Lab Automation". During the four-day intensive course held from March 23 to 26 at the Fritz Haber Institute, the doctoral students first learned the basics of programming in Python and gained insights into theoretical concepts of machine learning, after which they applied the skills they had acquired to specific problems.
During the first two days, the doctoral students learned the basics of Python: short, focused lectures alternated with practical, guided coding sessions to ensure that participants could immediately apply what they had just learned. To wrap up the first part of the program, the doctoral candidates were treated to a social event for networking and exchanging ideas.
The second part of the intensive course covered various areas of machine learning as well as the fundamentals of laboratory automation. The doctoral students gained insights into typical applications of machine learning, the mathematical foundations of prototypical algorithms and models, and became familiar with well-known toolkits. The workshop segment on laboratory automation focused on connecting typical laboratory equipment to Python and implementing small-scale use cases. The doctoral students then deepened their knowledge through group work by implementing specific machine learning problems.
The IMPRS-EPPC is a structured graduate program supported by the Fritz Haber Institute, the University of Potsdam, and the three Berlin universities (FU, HU and TU Berlin). In addition to soft skills seminars and further education courses, the school offers two block courses each year, typically comprising a full week of lectures that cover both fundamental and advanced methods in physical chemistry.
We are pleased to strengthen doctoral training through the support of IMPRS‑EPPC. Active peer exchange, comprehensive training opportunities, and close mentorship from the graduate school greatly enhance doctoral education.
