Postdoc in Deep learning for protein-protein interactions

School of Electrical Engineering and Computer Science at KTH

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.

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.

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 Ph.D. students and one senior researcher.

Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education, and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information, and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems, and software for the benefit of the Swedish industry. 

What we offer

• A position at a leading technical university that generates knowledge and skills for a sustainable future
• Engaged and ambitious colleagues along with a creative, international and dynamic working environment
• Works in Stockholm, in close proximity to nature
• Help to relocate and be settled in Sweden and at KTH
• Access to a GPU cluster with 60 DGX-A100 compute nodes

Read more about what it is like to work at KTH

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline (With some exceptions for special reasons such as periods of sick or parental leave, kindly indicate if such reason exists in your resume).
• Scientific skill
• Experience with machine learning methods

Preferred qualifications

• Good programming skills and knowledge of proteins as well as deep learning is an advantage.
• Teaching abilities.
• Awareness of diversity and equal opportunity issues, with a specific focus on gender equality.

As a person, you are creative and work with great independence. It’s important that you can collaborate both with colleagues as well as external partners. Great emphasis will be placed on personal competency.

Trade union representatives

You will find contact information to trade union representatives at KTH’s webbpage.

Application

Log into KTH’s recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad.

• CV including relevant professional experience and knowledge.
• Copy of diplomas and grades from your previous university studies. Translations into English or Swedish if the original documents have not been issued in any of these languages.
• A brief account of why you want to conduct research, your academic interests, and how they relate to your previous studies and future goals. Max: 2 pages long.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).