Dr Tim Meyer, Research Group Leader Pharmaceutical Biotechnology at University Medical Center Göttingen, explains why the next generation of scientists must be trained differently for the lab.  

Academic life science research has traditionally been optimised for novelty. Students and early-career researchers are trained to design elegant proof-of-concept experiments, execute them manually, and publish quickly. This model has been enormously successful at generating biological insight. But it rarely prepares scientists for what comes next: scalability, standardisation, data robustness, and regulatory expectations. As a result, many promising assays and models reach publication but fail to translate into reliable, industry-ready discovery platforms. 

At the same time, automation, digital lab infrastructure, and AI-assisted experimentation are rapidly reshaping drug discovery. Modern laboratories are no longer collections of isolated instruments, but interconnected systems that generate large, structured datasets and increasingly execute experiments with minimal human intervention. In this environment, scientific progress depends as much on workflow design, data integration, and process stability as on biological creativity. 

This creates a growing mismatch. While drug discovery laboratories are becoming more automated, most life-science education still focuses on manual techniques and single-experiment thinking. Scientists graduate with a rich scientific background and highly skilled at pipetting and microscopy, but with little exposure to automated workflows, digital lab systems, or the principles that make experiments scalable and reproducible, as seen by inter-laboratory reproducibility as low as 50% in some areas. 

For automation to truly transform drug discovery – making science faster, more reproducible, and less animal-dependent – we must rethink how we train scientists from day one. Training the next generation of scientists is no longer just about teaching them how to use instruments. It is about teaching them how to design experiments that machines, software, and data systems can run, monitor, and continuously improve. 

Building a fully automated 24/7 laboratory for education remains beyond the reach of most universities. A more realistic solution is to establish distributed smartlab hubs – shared sandbox laboratories that combine digital learning with hands-on access to advanced automation, similar to X-Labs where STEM students gain experience with equipment beyond the reach of individual schools. 

In this model, students could design and program automated assays remotely, execute them in a regional hub, and analyse the results the next day. Equipped with modern systems for liquid and plate handling, incubation, analytics, and sequencing, these hubs would introduce workflow-level automation into undergraduate training. Based on standardised hardware and open interfaces, each location could develop regional specialisations – from cell culture and pharmacology to synthetic biology or hardware development – while remaining interoperable within a growing open laboratory ecosystem. 

As of now every institution uses its own axe to grind, often underwhelming results. For sustainable progress we need a coordinating platform, to bundle ongoing efforts, forge new collaborations and provide room for discussion on common protocols and standards for laboratory open-source hard- and software to be used in educational setups. 

On the research side of things leading institutions have joined forces and created the Global Biofoundries Alliance to leverage synergies through open data and material ecosystems, e.g. through the active support and development of  standardised data models (e.g. synthetic biology open language) and material transfer agreements (openMTA)  

Following their example we must urgently bridge the divide between ambition and academic reality and join forces to form an inter-institutional education alliance for advanced laboratory technology and automation. The smartlab.network was formed to promote open hard- and software standards, to promote collective action and provide coordination between members. We now urge all interested parties to join the network, share their pain-points and seek, collectively, for solutions, financial and organisational, to establish virtual and real-world smartlab.hubs across borders to forge the next generation of life science professionals.

About the author

Dr Tim Meyer is a pharmacologist and laboratory automation specialist. As Head of Automation at myriamed since 2019 and Group Leader at the University Medical Center Göttingen’s Department of Pharmacology since 2018, he drives innovations in decentralised laboratory automation and extrusion 3D bioprinting of human heart muscle.

From DDW Volume 27 – Issue 2, Spring 2026 – Read the digital issue here