Job description
We recruit two postdocs on “Deep learning for protein-protein interactions” for two years financed by a joint grant from WASP and DDLS. One will be hired in Azizpour’s group at KTH within this call and the other at Elofsson’s group at Stockholm University call. We foresee both postdocs working closely together and with both groups.
Protein structure is essential for understanding their function as well as for developing drugs targeting proteins. Recently, a deep learning method that can predict the structure of most proteins was made freely available. However, proteins do not act alone – they act together with other proteins. Therefore, the next major challenge is to use these types of methods for predicting protein-protein interactions. Initial studies from us have shown that it is possible to predict accurate structures of a large part of dimeric proteins using either a modified version of AlphaFold2 or AlphaFold-multimer. However, there are still many proteins that cannot be built accurately, nor are we able to always distinguish interacting from non-interacting protein pairs, and to build larger complexes accurately is still an unsolved problem. In this project, we are recruiting two postdocs to leverage recent advances in the field of machine learning to build better deep-learning models for predicting protein-protein interactions and to apply these methods to biologically relevant problems.
Azizpour’s group is part of the KTH division of Robotics, Perception, and Learning. He has extensive experience in computer vision and deep learning. The main research directions pursued in Azizpour’s group have direct relevance to this project which includes robustness and estimation of uncertainty, transfer learning including knowledge distillation techniques, non-standard deep networks e.g., graph networks and transformers, and interpretable deep learning. Furthermore, the group has extensive experience in deploying large experiments in GPU clusters. It consists of 4 Ph.D. students, 1 postdoc, and several master students/interns.