Ekkehard Ullner

Dr. Ekkehard Ullner is a Lecturer in the Department of Physics at the University of Aberdeen, affiliated with the Institute for Complex Systems and Mathematical Biology (ICSMB) and the School of Natural and Computing Sciences. He has been a SULSA Lecturer for Systems Biology since 2003 and is actively involved in research, teaching, and postgraduate supervision.

His educational background includes a Dr. rer. nat. (summa cum laude) from the University of Potsdam, Germany, with a thesis on noise-induced phenomena in neural models, supported by an International Max Planck Research School stipend. He also held a research fellowship from the Alexander von Humboldt Foundation.

Dr. Ullner's research centers on nonlinear dynamics and stochastic processes in biological systems. Key areas include balanced neuronal networks, synthetic genetic oscillators, collective dynamics in spiking neurons, noise-induced phenomena, and modeling of complex biological systems. His work bridges physics, biology, and computational modeling.

His recent publications (2014–2024) reflect a strong focus on collective neural dynamics, noise-induced computation, aging propagation in biological networks, and robust control in gene expression. The research demonstrates interdisciplinary collaboration and a consistent application of statistical physics to biological problems.

• Alexander von Humboldt Foundation Research Fellowship
• Dr. rer. nat. summa cum laude (University of Potsdam)

Dr. Ullner supervises PhD students in Physics and related fields, particularly in data science, statistical physics, and modeling biological systems. He is also the Director of Postgraduate Programmes in the Department of Physics and serves as Postgraduate Research School Officer. He teaches courses such as Relativity and Quantum Mechanics (PX2510), Quantum Mechanics (PX3511), and Statistics and Time Series Analysis (PX5010).

He is associated with the SyMBioSIS research group and contributes to interdisciplinary initiatives at the intersection of physics, biology, and data science.