Umeå University, Department of computing science

Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture.

The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here.

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Umeå University, the Department of Computing Science, is seeking outstanding candidates for a postdoc position in robust machine learning and data-centric optimization with focus on scarce data and non-standard model settingsDeadline for application is August 10, 2020.

Project description

We invite candidates to apply for one postdoc position funded by The Knut and Alice Wallenberg Foundation through The Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest ever individual research program, and a major national initiative for strategic basic research, education and faculty recruitment. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society as well as industry. For more information about the research and other activities conducted within WASP please visit

The rapid increase of autonomous systems and applications are providing challenges in dealing with both scarce and petabytes of data in diverse environments. Machine learning has recently achieved a lot with the standard assumption that availability of data is large. However, it still remains many challenges in areas where this is not true. These sizes and heterogenous features make the machine learning models larger and more complex. Classical approaches to training, learning, and inference fail to address the problems of scarce data, non-standard model settings, data-centric optimization (centralized and distributed), communication, computation, synchronization, and many more. This project focuses on design and implementation of robust machine learning and data-centric optimization algorithms for scarce and petabytes of data and non-standard model settings, which are ideally suited for constraint environments and edge infrastructures.

This project aims to design and implement robust learning and data-centric optimization techniques for advancing state-of-the-art machine learning algorithms where data is geographically distributed, sensitive, and scarce. Robust machine learning and data-centric optimization algorithms empower models through multi-level (local, global and hybrid) training, learning, and inference with data-centric optimization for scarce data and non-standard model settings. By creating unique features (e.g., decentralized training, learning and inference, fault-tolerant against failures and attacks, data-centric optimization, robustness), this project addresses the challenges in the following areas: robust learning; learning with scarce data and non-standard model settings; lack of theoretical knowledge to build manual models; computation efficient learning and optimization for obtaining more accurate and robust models with applications to constraint environments (i.e., Industrial Internet of Things (IIoT), healthcare systems) and edge infrastructures.

The position is at the Department of Computing Science at Umeå University, Sweden. The selected candidate is expected to contribute towards the local research community by actively participating in the departmental and group research activities such as workshops, seminars, etc. These contributions can be within autonomous distributed systems lab, but collaboration with researchers in, e.g., machine learning, mathematical statistics, optimization, trustworthy learning and artificial intelligence is expected. (For further information, see


Applicants must have earned a PhD or a foreign degree that is deemed equivalent to a PhD in Artificial Intelligence, Machine Learning or Optimization for Machine Learning, Computer Science or a subject relevant for the position. The PhD degree should not be more than three years old by the application deadline, unless special circumstances exist.

Candidates are expected to have outstanding knowledge of machine learning and optimization techniques. Demonstrable knowledge of data wrangling and learning in decentralized settings is a prerequisite. Experience in any of the areas robust learning, fault-tolerant learning and data-centric optimization when data is geographically distributed, sensitive and scarce is a merit.

Since research is conducted in an international research environment, the ability to collaborate and contribute to teamwork, and an excellent command of the English language, both written and spoken, are essential requirements.

We particularly invite female candidates to apply to ensure gender balance.

Terms of employment

The appointment is for two years full-time employment. Postdocs are typically offered the opportunity to gain teaching experience on suitable undergraduate courses. Expected starting date is October 1 or as otherwise agreed.

Submitting your application

A complete application should contain the following documents:

  • Introductory letter including a 2-page statement of research interests relative to the above topics and a motivation of why your expertise is appropriate for the position.
  • Curriculum Vitae (CV) including a complete list of scientific publications.
  • Copies of degree certificates, including documentation of completed academic courses and obtained grades
  • A copy of your PhD thesis and copies of (max 5) original research publications relevant to the above topics, numbered according to the publication list.
  • Names and contact information for three persons willing to act as references.
  • Any other information relevant for the position such as description of software development experience, or previous industry experience.

The application must be written in English or Swedish. Documents must be in Word or pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than August 10, 2020.

Further information can be obtained from Assistant Professor Monowar Bhuyan, (email: and Professor Erik Elmroth (email:

More about us:

The Department of Computing Science is a dynamic environment with over 130 employees representing more than twenty countries worldwide. We conduct education and research on a broad range of topics in Computing Science. The focus of the research in the Autonomous Distributed Systems Lab is to design, develop, deploy distributed learning algorithms for (autonomous) resource and application management for different types of IoT, clouds and distributed systems.

We look forward to receiving your application!

Type of employment Temporary position
Contract type Full time
First day of employment September 1, 2020 or as otherwise agreed
Salary Monthly
Number of positions 1
Full-time equivalent 100
City Umeå
County Västerbottens län
Country Sweden
Reference number AN 2.2.1-938-20
  • Monowar Bhuyan, 090-786 67 05
  • Erik Elmroth, 090-786 69 86
Union representative
  • SACO, 090-786 53 65
  • SEKO, 090-786 52 96
  • ST, 090-786 54 31
Published 17.Jun.2020
Last application date 10.Aug.2020 11:59 PM CEST

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