Helmholtz Munich Wins Three New ERC Starting Grants

Matthias Tschöp, CEO at Helmholtz Munich:

“With a total of 42 ERC grants acquired Helmholtz Munich achieves an outstanding result that confirms our successful research strategy at the international level. Excellence in basic research is the foundation for successful transfer to patients.”

The aim of ERC Starting Grants is to support early career researchers in establishing their own careers and making the transition to independent and autonomous research. Applicants of any nationality must have two to seven years of postdoctoral experience and a promising scientific track record. Outstanding research projects will be funded with a sum of up to 1.5 million euros over a maximum project period of 5 years. Two of the three awarded researchers are taking up the successfully acquired grants at Helmholtz Munich.Dominik Jüstel,

Computational Health & Bioengineering:

Radiomics, the extraction of medical information from imaging data via mathematics and data science, is on the verge of becoming a main player in clinical medicine. However, the current radiomics workflow lags behind the state-of-the-art in explainable artificial intelligence. Dominik Jüstel will integrate the whole imaging workflow – from imaging hardware to clinical interpretation – into an intelligent software environment, thereby realizing the transition from black box machine learning to intelligent radiomics. The clinical use case is imaging of peripheral nerves with optoacoustic tomography, which can visualize nervous tissue in unprecedented detail. Jüstel’s approach, thus, has the potential to enable early detection of pathological changes in peripheral neuropathy, e.g., in conjunction with diabetes.

Nicolas Battich, Computational Health, Stem Cells & Pioneer Campus:

Understanding how stem cells develop into all the different cell types in the body holds great potential for regenerative medicine. Artificial methods for cell differentiation in the laboratory do not yet produce the desired quality of cells and are usually inefficient. Human stem cells differentiate into other cells by controlling changes in their gene expression. Nicolas Battich wants to study this control in more detail by focusing on how nuclear sub-compartments affect the conformation of the genome and the dynamics of gene expression. To do this, he will develop new single-cell sequencing methods, use tools such as CRISPR-Cas9, and create computational tools based on state-of-the-art deep learning methods. His goal is to understand how stem cells control gene expression to then improve the generation of desired cell types in the laboratory to aid regenerative medicine.