Daniel Markl’s research focuses on developing predictive systems, in-silico design methods and self-driving labs for drug product development and manufacturing that accelerate the pace at which new medicines are developed and delivered. He obtained a BSc (2010) and MSc (2012) in telematics with a focus on neural networks and a PhD (2015) in chemical engineering from Graz University of Technology. Daniel secured an Erasmus Mundus in 2010, which allowed him to study modelling and control system design for one year during his MSc at Lund University. During his PhD he was employed by the Research Center Pharmaceutical Engineering (RCPE) GmbH, where he was in the group Process and Manufacturing Science and involved in several projects at the interface of pharmaceutical engineering, materials science and process modelling. He continued as Senior Scientist and Scientific Project Leader at RCPE after completing his PhD. In 2016 he joined Professor Zeitler’s group (Terahertz Applications Group) as a postdoctoral research associate at the University of Cambridge. Daniel worked for two years in the Terahertz Applications Group before becoming a Chancellor’s Fellow and Lecturer/Assistant Professor at the University of Strathclyde in the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS).

Daniel is Associate Director at CMAC (www.cmac.ac.uk), Training Director of the EPSRC Centre of Doctoral Training in Cyberphysical Systems for Medicines Development and Manufacturing and leads the MHRA-funded Centre of Excellence in Regulatory Science and Innovation (CERSI) for the digital transformation of medicines development and manufacturing. 

Our laboratory conducts multidisciplinary research at the intersection of materials, processes, products, and performance, with the goal of transforming how medicines are designed, developed, and manufactured. We seek to establish a fundamental, predictive understanding of how material properties and processing conditions jointly determine product structure, quality, and performance.

Central to our approach is the coupling of self-driving laboratories, advanced measurement techniques, and digital process and product design. By integrating automation, high-throughput experimentation, real-time analytics, and data-driven modeling, we aim to dramatically accelerate development timelines while improving robustness, efficiency, and knowledge generation across the pharmaceutical lifecycle.

Our research includes the development of predictive systems, in-silico design methods, and autonomous experimental platforms for drug product development and manufacturing. These tools enable rapid exploration of complex formulation and process spaces, support rational decision-making, and reduce reliance on trial-and-error experimentation. Ultimately, our work lays the foundation for adaptive, intelligent manufacturing systems that deliver higher-quality medicines more efficiently and reliably.

Key expertise and capabilites:

• Materials–Process–Product–Performance Relationships
  Fundamental and applied understanding of how material properties and processing conditions govern drug product structure, quality, and performance.

• Self-Driving and Autonomous Laboratories
  Design and deployment of closed-loop experimental platforms that integrate automation, real-time analytics, and machine learning for accelerated pharmaceutical development.

• Advanced Measurement and Characterization
  Development and application of high-resolution, high-throughput measurement techniques to capture critical material, process, and product attributes.

• Predictive Modeling and In-Silico Design
  Data-driven and physics-informed models for virtual formulation and process design, optimization, and scale-up.

• Digital Process and Product Design
  Integration of computational tools, experimental data, and automation to enable rational, efficient drug product development and manufacturing.

• Pharmaceutical Manufacturing Innovation
  Intelligent, adaptive manufacturing strategies aimed at improving robustness, efficiency, and product quality while reducing development time and risk.