Till Muser

Data Scientist

Research

Alumni

(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

Completed

Machine learning for the components fatigue prediction in hydropower generation

Academia

MUTIGER

In Progress

MUTations, Interactions and GEne Regulation

Academia

Publications

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Grover, A.; Muser, T.; Kasak, L.; Zhang, L.; Krymova, E.; Boeva, V. "Early feature extraction drives model performance in high-resolution chromatin accessibility prediction" Genome Research gr.281042.125 2026 View publication 

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Grover, A.; Muser, T.; Kasak, L.; Zhang, L.; Krymova, E.; Boeva, V. "Evaluation of deep learning approaches for high-resolution chromatin accessibility prediction from genomic sequence" Preprint 2025 View publication 

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Muser, T.; Krymova, E.; Morabito, A.; Seydoux, M.; Vagnoni, E. "Fatigue damage reduction in hydropower startups with machine learning" Nature Communications 16 1 2961 2025 View publication 

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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 12043 2024 View publication 

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Grover, A.; Zhang, L.; Muser, T.; Häfliger, S.; Wang, M.; Yates, J.; Van Allen, E. M.; Theis, F. J.; Ibarra, I. L.; Krymova, E.; et al. "UniversalEPI: a generalized attention-based deep ensemble model to accurately predict enhancer-promoter interactions across diverse cell types and conditions" Preprint 2024 View publication 

Mentioned in

May 1, 2025

PAIRED-HYDRO | Increasing the Lifespan of Hydropower Turbines with Machine Learning

Hydropower accounts for 60% of Swiss energy production, with significant potential for growth.

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Case Studies

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