Brain injury and neurodegeneration cause irreversible neuron loss. This project models the adult human brain injury environment by decellularizing adult human brain tissue from patients and characterizing its extracellular matrix (ECM). Neuronal replacement strategies will be tested, including transplantation of human iPSC-derived neurons and reprogramming of iPSC-derived or adult astrocytes isolated from resection material, to define conditions that support neuronal replacement across diseases and ages.

Quantum computing has the potential to provide speedups for classical problems such as complex simulation, optimization and cryptography tasks, but requires scalable and reliable qubit technologies. Semiconductor spin qubits combine long coherence times with established industrial fabrication techniques. This project focusses on implementing high-fidelity two-qubit gates, which are then expanded to multi-qubit arrays and quantified by applying quantum information algorithms together with rigorous benchmarking.

This project explores how subcortical structures such as the amygdala, thalamus, basal ganglia, cerebellum and hippocampus shape social cognition. Using high-resolution fMRI, diffusion MRI and computational modeling it examines how connections between subcortex and cortex support empathy, perspective taking and emotional regulation.

Visual perceptual learning (VPL) – a type of visual skill learning – has often been studied in adults as a model system to identify neural mechanisms of learning. However, it is unclear whether children employ similar neural mechanisms for VPL as adults. In this project, we will use non-invasive brain imaging to measure and compare neural mechanisms of VPL in children and adults and examine whether and if so how learning changes from childhood to adulthood.

The cytotoxicity of organic tin compounds makes them interesting for cancer chemotherapy. Organotin chalcogenide clusters release under acidic decomposition not only an organic tin compound but also an even more toxic hydrogen chalcogenide. To enable biocompatibility of those clusters, they must be derivatized with biomolecules. This project aims to synthesize biofunctionalized organotin chalcogenide clusters and to investigate their decomposition behavior.

Mitochondrial protein import is mediated by the presequence translocase of the inner membrane (TIM23 complex). While Tim17 is central for inner membrane translocation, the exact role of Mgr2, a small transmembrane protein, remains unclear. This project aims to define the function of Mgr2 in precursor protein import, membrane insertion, and translocase stability using genetic, biochemical, and structural approaches.

Non-electric actuators offer a promising alternative for sustainable and remote operations. My research focuses on the development of thermal micro-actuators based on thermomagnetic thin films, which harness the intrinsic material property of magnetization loss near the Curie temperature to achieve controlled mechanical motion. Unlike conventional actuators that rely on electrical stimulation, these devices operate by thermal triggering, eliminating the need for continuous electrical input and thereby reducing power consumption.

This project investigates how sulfur-containing amino acids regulate tRNA thiolation in endothelial cells, thereby controlling protein translation during vessel growth. Mapping the human endothelial tRNA thio-epitranscriptome will uncover novel translational control mechanisms and lay the foundation for potential therapeutic strategies targeting pathological angiogenesis.

This project investigates the mutualism between Vachellia trees and Pseudomyrmex ants, focusing on their role in wound care. Using chemical ecology, proteomics, microbiology, and behavioral experiments, we plan to identify relevant wound healing compounds as well as the evolutionary mechanisms that enabled inter-kingdom wound care. This research expands on our concept of the social immune system by expanding it towards the ants’ host in this unique relationship.

This research projec aims to contribute to the development of methods that extract informative and interpretable features from high-dimensional datasets, with a focus on uncovering high-level causally related factors that describe meaningful semantics of the data. This, in turn, can help us gain deeper insights into the representations found within advanced generative models, particularly foundation models and LLMs, with the goal of improving their efficiency and safety.