Till Muser

Till Muser

Data Scientist
Academia
(Alumni)

Till obtained his Bachelor's and his Master's degrees in Physics at ETH Zurich in 2020 and 2022 respectively. Over the course of his studies and by applying computational methods to problems in physics, he developed a fascination with data science and machine learning. Having joined the SDSC in July 2022, Till seeks to gain an impression of academia outside the physics domain, as well as a deeper understanding of data-driven problem-solving. In his work, Till is using Deep Learning methods to lengthen the lifespan of hydropower turbines and to better understand the effect of mutations in non-coding regions of the DNA.

Projects

PAIRED-HYDRO

In Progress
Machine learning for the components fatigue prediction in hydropower generation
Engineering

MUTIGER

In Progress
MUTations, Interactions and GEne Regulation
Biomedical Data Science

Publications

Grover, A.; Zhang, L.; Muser, T.; Häfliger, S.; Wang, M.; Yates, J.; Van Allen, E. M.; Theis, F. J.; Ibarra, I. L.; Krymova, E.; Boeva, V. "UniversalEPI: a generalized attention-based deep ensemble model to accurately predict enhancer-promoter interactions across diverse cell types and conditions" Preprint, View publication
Vagnoni, E.; Muser, T.; Seydoux, M.; Morabito, A.; Krymova, E. "On the modelling of the fatigue-induced damage in Francis turbines start-up sequences" IOP Conference Series: Earth and Environmental Science, 1411 1.0 012043 View publication

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