Postdoctoral Fellow Clara Wanjura honored with dissertation prize of the German Physical Society

The German Physical Society awarded Dr. Clara Wanjura with the SAMOP Dissertation Prize 2024. The scientist received the prize for her doctoral thesis, which she wrote at the University of Cambridge under Prof. Andreas Nunnenkamp. Wanjura is currently conducting research as a Postdoctoral Fellow in the Theory Division of the Max Planck Institute for the Science of Light under Institute Director Prof. Florian Marquardt.

Dr Clara Wanjura, Max Planck Institute for the Science of Light (centre) together with DPG President Prof. Dr Joachim Ullrich (left) and Prof. Dr Dr Gereon Niedner-Schatteburg, spokesperson of the DPG AMOP section (right). © DPG / Daab 2024

At the ceremony in Freiburg, the theoretical physicist was given the prestigious award by the Atoms, Molecules, Quantum Optics and Photonics Section (SAMOP) for her outstanding scientific work and its excellent presentation.

In her dissertation “Non-Hermitian Topology and Directional Amplification in Driven-Dissipative Cavity Arrays“ Wanjura showed that directional amplification is the experimental signature of non-Hermitian topology. Directional amplification, in which signals are selectively amplified depending on their propagation direction, has attracted much attention as a key resource for applications including quantum information processing. Wanjura's work provides the basis for novel directional amplifiers and sensors based on the principle of non-Hermitian topology.

Every year, a jury formed by representatives of the SAMOP research areas selects four finalists for the dissertation award. The finalists are invited to present their research at the dissertation prize symposium. Following the symposium, the awardee is selected by the prize committee.

Wanjura will continue her research at the Max Planck Institute for the Science of Light in the field of non-Hermitian topology. Most recently, she expanded her research area to neuromorphic computing, in which physical processes are used to mimic neural networks. The aim is to find more efficient solutions for machine learning and artificial intelligence.



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