- Data och IT
Ansök senast: 2022-03-18
Postdoc in Deep learning for protein-protein interactions
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.
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 developed. However, proteins do not act alone, thus, the next major challenge is to predict protein-protein interactions. Despite the recent advances, there are many proteins that cannot be built accurately, nor are we able to always distinguish interacting from non-interacting protein pairs. 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 project is in close collaboration with Arne Elofsson’s group at SciLifeLab.
This position is part of a joint collaboration between the two largest research programs in Sweden, the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), with the ultimate goal of solving ground-breaking research questions across disciplines.
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. Read more: https://wasp-sweden.org/
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 program 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: https://www.scilifelab.se/data-driven
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
- 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
- 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.
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).
About the employment
The position offered is for, at the most, two years.
A position as a postdoctoral fellow is a time-limited qualified appointment focusing mainly on research, intended as a first career step after a dissertation.
Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.
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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.