-
project 1. Only 2% of the human genome sequence codes for proteins, while most of it consists of noncoding sequences, including regulatory factor binding regions, transposable elements, pseudogenes
-
predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will
-
good software engineering habits — modular, well-documented, reproducible code. Are comfortable working in interdisciplinary teams and can communicate effectively across computational and experimental
-
engineering practice, collaborative development experience (e.g. Git/GitHub), and ability to use AI-assisted coding tools effectively. Strong communication and collaboration skills, with the ability to work