Uppsala University is seeking a PhD student in Machine Learning methods.
Project description
This project focuses on developing, analyzing and using probabilistic methods for dynamic phenomena evolving over space and time based on measurements from different and complementary sources. We will develop generally applicable machine learning models and methods driven by the data-rich experiments from our collaborators. The real-world use-case is to learn the rules that govern the dynamics of the bacterial chromosome structure.
Technical building blocks could include state-space models, generative models in the form of diffusion models, deep learning, optimal transport, and probabilistic modelling in general. Computer vision can also be included if there is interest.
Uppsala offers a PhD student position to explore and develop Machine Learning models evolving over space and time and to make use of these models to understand the organization of DNA and its relation to the dynamic 3D-structured chromosomes. The student will form a part of our new NEST initiative funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the Wallenberg National Program for Data-Driven Life Science (DDLS). Our project, Learning 3D Genome Dynamics from Heterogeneous Data, is a 5-year collaboration between researchers at Uppsala University and Karolinska Institute. The overall objective is to develop and make use of machine learning methods to help us understand the organization of life.
