Postdoctoral Fellow in Deep Learning for Protein Protein Interactions

Stockholm University

Application deadline

January 31, 2022

Postdoctoral Fellow in Deep Learning for Protein–Protein Interactions

Department of biochemistry and biophysics, Stockholm University

The Department is mainly located with the other Departments of Chemistry and Life Sciences in the Arrhenius Laboratories for Natural Sciences, which are situated in the northern part of the University Campus at Frescati. Presently more than 300 people are working at the Department, of which about 110 are PhD students engaged in internationally highly recognized research covering a broad range of subjects. Please read more at:

Project description
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 and a database with predicted structure was released. 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.

DDLS: The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a 12-year initiative that focuses on data-driven research, within fields essential for improving people´s lives, detecting and treating diseases, protecting biodiversity and creating sustainability. The programme will train the next generation of life scientists and create a strong computational and data science base. The program aims to strengthen national collaborations between universities, bridge the research communities of life sciences and data sciences, and create partnerships with industry, healthcare and other national and international actors. Read more at:

Environment: The Elofsson group is located at the Science for Life Laboratory. Elofsson has worked on protein structure predictions for more than two decades. He has worked on various techniques, both using machine learning and other computational techniques. His most important contributions for this work are the methods he has developed to identify the quality of protein models, Pcons and various versions of ProQ. The group consists currently of 5 PhD students and one senior researcher. 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 PhD students, 1 postdoc, and several master students/interns.

Resources: The groups have access to the Berzelius computer (funded by KAW). Berzelius is an NVIDIA® SuperPOD consisting of 60 NVIDIA® DGX-A100 compute nodes supplied by Atos. Each DGX-A100 node is equipped with 8 NVIDIA® A100 Tensor Core GPUs, 2 AMD Epyc™ 7742 CPUs, 1 TB RAM and 15 TB of local NVMe SSD storage. The A100 GPUs have 40 GB on-board HBM2 VRAM.

Selected references:

  • Bryant, P, Pozzati, G. & Elofsson, A. “Improved prediction of protein-protein interactions using AlphaFold2” bioRxiv 2021.09.15.460468 (2021) doi:10.1101/2021.09.15.460468.
  • David F. Burke, Patrick Bryant, ….Arne Elofsson “Towards a structurally resolved human protein interaction network” bioRxiv 2021.11.08.467664; doi:
  • Mehmet Akdel, …. Arne Elofsson, Tristan I Croll, Pedro Beltrao “ A structural biology community assessment of AlphaFold 2 applications” bioRxiv 2021.09.26.461876; doi:
  • Federico Baldassarre, David Menéndez Hurtado, Arne Elofsson, Hossein Azizpour“ GraphQA: protein model quality assessment using graph convolutional networks“ Bioinformatics, Volume 37, Issue 3, 1 February 2021, Pages 360–366,
  • Erik Englesson, Hossein Azizpour, “Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation”, ICML 2019 Uncertainty in Deep Learning workshop

This is a recruitment which is part of a joint grant for two postdocs this one at Stockholm University finances by DDLS and one at KTH finances by WASP.

Main responsibilities
The position involves research with a focus to develop novel state-of-the-art deep learning-based methods mainly focused on protein–protein interactions.

Qualification requirements
Postdoctoral positions are appointed primarily for purposes of research. Applicants are expected to hold a Swedish doctoral degree or an equivalent degree from another country.

Assessment criteria
The degree should have been completed no more than three years before the time when the employment decision is made. An older degree may be acceptable under special circumstances. Special reasons refer to sick leave, parental leave, elected positions in trade unions, service in the total defense, or other similar circumstances as well as clinical attachment or service/assignments relevant to the subject area.

In the appointment process, special attention will be given to research skills in protein bioinformatics, good programming skills, practical experience in deep-learning preferable within life science.

Terms of employment
The position involves full-time employment for a maximum of two years, with the possibility of extension under special circumstances. Start date 2022-04-01 or as per agreement.

Stockholm University strives to be a workplace free from discrimination and with equal opportunities for all.

Further information about the position can be obtained from Professor Arne Elofsson, telephone: +46 70 695 10 45,

Last updated: 2021-12-17

Content Responsible: David Gotthold(