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.

Amongst the leading causes of retinal dystrophies worldwide is Retinitis pigmentosa, a severe disease often caused by splice variants in the USH2A gene. This project aims to develop a safe CRISPR-based therapeutic strategy for correction of such splicing defects. Using enhanced-deletion nucleases, the disease-causing alterations can be eliminated, hereby restoring correct protein synthesis. The focus lies on safety features as well as the development of an inducible viral delivery system for clinical application.

The research project investigates the influence of sterically demanding NHC-gold(I) complexes on the cyclization of diyne derivatives. The focus is on the synthesis of various sterically demanding NHC-gold(I) complexes and their application in diyne cyclizations, particularly examining the reactivity and selectivity in gold-catalyzed reactions. Further investigations include theoretical calculations and practical applications of the synthesized cyclization products for pharmaceuticals or organic materials.

This project seeks to advance rare disease classification using deep neural networks by addressing key challenges such as limited data and high heterogeneity. We will assess existing models and their representations, investigating how technical variations in medical data affect their characteristics.

We aim to develop artificial neural networks with brain-inspired circuits. Like in the brain, artificial neurons and synapses are built with novel memristors, arranged in a crossbar array. By combining these with ultra-fast photonics, we seek to improve signal processing and matrix-vector multiplications to address limitations from state-of-the-art architectures. This approach aims to advance computing solutions by reducing energy consumption, computational time, and system complexity.

My doctoral project focuses on identifying light-sensitive proteins called photoreceptors in the model organism Chlamydomonas reinhardtii. I aim to answer how these receptors regulate the inner biological clock known as the circadian rhythm. My major focus is to determine the properties of an unknown red light-sensitive photoreceptor and how this receptor regulates the clock. These insights can be used to understand how plants, in general, process light information.

This project focuses on the distribution of deep-sea macrofauna (animals between 0.3 mm – 5 cm) in the deep Arctic Ocean across temporal and spatial scales. I will test the hypothesis if environmental factors like ocean warming and ice retreat will affect community composition. In addition, I will study the diversity, distribution, and connectivity of isopods as they comprise an abundant and diverse group of the macrofauna but are understudied in the Central Arctic. They are “brooders” – meaning they hatch their young in a brood pouch (imagine a tiny kangaroo) and therefore they usually do not disperse as far as animals with free-swimming larvae.

3D printing at the nanoscale is an established technology. However, for certain applications it is still considered prohibitively slow. Conventionally, laser beam pulses illuminate one volume element after another in a light-sensitive ink, building up the desired object. In this project, each laser pulse is holographically shaped and illuminates a large number of voxels in parallel. This technique promises orders of magnitude faster printing and shall be demonstrated for complex 3D structures.

Polar molecules enable the simulation of complex spin models and condensed matter phenomena due to their tunable long-range interactions. This project aims to investigate the transition from a Bose-Einstein condensate of tetratomic molecules to a diatomic p-wave superfluid, referred to as BEC-BCS crossover. The superfluid is of special interest as it is expected to host Majorana zero modes—quasi-particles ideal for fault-tolerant topological quantum computing.

The project aims to accelerate and improve the fabrication of micromaterials by 3D laser printing through the use of deep neural networks (DNNs). Physical simulations of the printing process are developed and used to train the DNNs. They can then, for example, characterize the printed structures already in the printer or pre-compensate objects in such a way that iterative characterization and optimization outside the printer can be minimized.