The project is developing a machine learning framework to accurately predict the excited states of rhodopsins. To this end, a dataset of quantum chemical calculations on retinal derivatives in protein-like environments is being compiled and used for model training. The models are validated and refined through the repeated synthesis and spectroscopic analysis of specifically designed rhodopsin variants. The goal is to create a data-driven platform for the rational design of light-sensitive proteins and the accelerated development of new photoreceptors.

Modern photonic systems require precise thermal management to ensure stability and performance. This interdisciplinary project develops a new type of elastocaloric microcooling device capable of operating for more than one million cycles. By combining advanced materials research on shape memory alloy films with innovative device engineering and photonic system integration, the project aims to create a highly efficient solid-state cooling technology. The collaboration between KIT, ETH Zurich, and Fraunhofer IWM addresses key challenges in durability and reliability of microscale cooling technologies for next-generation photonic and neuromorphic systems.

The project is developing covalently linked porphyrin spin chains on ultrathin insulators to create designer quantum model systems. Using low-energy electrospray ion beam deposition (LEIBD), mass-selected metal-tetraphenylporphyrin fragments are selectively deposited onto MgO/Ag(100) or NaCl/Au(111) substrates and linked into short 1-D chains (2–6 units). Using ESR-STM and pulsed ESR techniques (Rabi, Ramsey, Echo), the g-factor, exchange, and dipole couplings are determined and the spins are coherently controlled, creating a versatile platform for molecular quantum simulators.

PRECISE CRC is developing an embedding-based system that transforms heterogeneous lifestyle and healthcare data from biobanks into structured patient summaries using a context-specific large language model (LLM), embeds these into latent vectors, and identifies true causal risk factors for colorectal cancer using advanced causal methods to identify true causal risk factors for colorectal cancer.