Meet Sarah Brüningk, Postdoctoral Researcher

Sarah is a postdoc working at the interface of computational biology andartificial intelligenceat ETH Zurich in the Machine Learning and Computational Biology lab.

As part of the NCCR AntiResist Project, she works as a data scientist, analyzing data produced within this consortium but also devising software solutions that may be useful in the field in general.

More about Sarah Brüningk

Originally from Munich, Germany, she also studied physics there at the Technical University of Munich. During high school, Sarah was fascinated by science and mathematics. She first couldn’t decide which branch of science to pursue at university, and decided that a degree in physics would allowed her to remain flexible with her career choice. She always thought she would move more in the direction of astro- or engineering physics, but during her Master studies, she discovered the number of medical applications for physics and was very interested to go deeper into this direction. Sarah began to focus on physics for cancer therapy (radiotherapy) and diagnosis (imaging). Since she really enjoyed her first research project during her Master’s thesis, she decided to pursue a PhD at the Institute of Cancer Research in London, UK. There, her project combined practical wet lab experiments with computational analysis and simulations aiming to better quantify the biological effects of combination therapies. Working in a biology lab was a big change for her at the time since she had never held a pipette before starting her PhD and suddenly spent most of her time working in a sterile hood. In the end it was a great asset to have hands-on experience of how the data she used for her simulations were generated and what uncertainties these measurements entailed. This experience really sets her apart from other computational researchers and she is quite proud of having produced publications in several disciplines.

After her PhD Sarah wanted to learn more about the area of artificial intelligence, since this was a topic she had not yet covered. Her postdoc research interests at the ETH Zurich are in combining machine learning and modelling for healthcare applications, embracing the clinical hallmarks of a disease in order to provide solutions that are understandable for clinicians and can be translated to clinical practice. In addition to her NCCR AntiResist work, Sarah is excited follow a more independent research idea as part of the Botnar Research Center for Child Health Postdoctoral Excellence Programme (BRCCH PEP).

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