Umeå University, Faculty of Science and Technology

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.

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The Department of Mathematics and Mathematical Statistics conducts research in computational mathematics, discrete mathematics, mathematical modelling and analysis, and mathematical statistics. Our teaching is conducted at all levels and includes mathematics, mathematical statistics and computational science. Among our partners are international research groups, academic institutions, public organizations and companies.

The Department of Mathematics and Mathematical Statistics at Umeå University is opening a postdoctoral position in mathematical statistics, within the programme Wallenberg AI, Autonomous systems and software program (WASP), with an emphasis on statistical learning with sparsity and beyond. Last day to apply is September 30, 2019.

The appointment is for two years at the Department of Mathematics and Mathematical Statistics. The successful candidate is expected to conduct excellent research, actively engage with collaborators, and to participate in the daily activities of the research environment. Starting date is fall 2019 or as otherwise agreed.

The expansion of Artificial Intelligence (AI), in the broad sense, is one of the most exciting developments of the 21st century. This progress opens up many possibilities but also poses grand challenges. The centre WASP is launching a program to develop the mathematical side of this area. The aim is to strengthen the competence of Sweden as a nation within the area of AI. This project is part of this program. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. For more information about the research and other activities conducted within WASP please visit

Project description and working tasks
Industrial robots, autonomous cars, stocks trading algorithms, and deep network assisted evaluation of medical images all crucially involve real-time, intelligent and automated decision making from complex and heterogeneous data, at ever growing scale and pace. This presents unprecedented theoretical and algorithmic challenges and opportunities for researchers in intelligently collecting and transforming data into information, predictions and intelligent decisions.

Modern statistical learning is at the forefront of these advances, and the main objective of this postdoctoral position is to develop cutting-edge mathematical and statistical theory and the next generation of computational tools to address the above challenges with an emphasis on learning with sparsity and other emerging data models, and to potentially explore their applications in AI, including medical imaging, automated quality control, and self-driving cars, evaluated on both simulated and real data.

Understanding and exploiting sparsity and other data structures to extract useful information from big datasets with the purpose of making optimal decisions will be the main research thrust for this position.

Within this broad framework, the successful candidate is encouraged to develop their own research agenda, in close collaboration with mentors and colleagues. Potential areas of interest include, but are not limited to:

  • Learning with multiple structures (such as sparsity and rank constraints)
  • Intelligent data sampling and uncertainty quantification in medical imaging
  • Time-data trade-offs in learning
  • Statistical learning with generative adversarial networks and their geometry,
  • Streaming and distributed algorithms for dimensionality reduction (such as sparse principal component analysis)
  • Defence against adversarial examples in deep neural nets.

The applicant must have earned a PhD or a foreign degree that is deemed equivalent to a PhD in mathematical statistics, applied mathematics or equivalent academic competence. Applicants must have completed their Ph.D. within three years of the application deadline but exceptions will be considered in case of illness, parental leave, clinical practice, positions of trust within labour unions, or other similar circumstances.

Documented knowledge and proven experience in modern statistical learning and optimization theory are required. In addition, excellent programming skills (preferably MatLab, Python and R) and excellent communication skills in written and spoken English are required.

The research tasks require great independence, accuracy and dedication. Documented scientific momentum and ability to work independently as well as part of a research group are merits.

In particular, excellent track record of publication and experience in statistical learning with sparsity and other emerging data models are strong merits. Experience of interdisciplinary research projects and cross-disciplinary collaboration, in particular within the specific application areas, is qualifying.

The application should contain the following documents:

  • A cover letter of one page describing yourself, your previous research achievements, your preferred area of scientific application, and your motivation for your interest in this position
  • A  Curriculum Vitae with a list of publications
  • Reinforced copies doctoral degree certificate and other relevant degree certificates as well as relevant grades
  • Copies of (maximum of five) relevant publications including your PhD thesis
  • A research plan, 3-4 pages in length, which describes your research interests and how to contribute to the research project
  • Contact details to two reference persons, familiar with your qualifications.
  • Other possible documents you wish to claim

The Department of Mathematics and Mathematical Statistics values ​​the qualities that an even gender distribution brings to the department, and therefore we particularly encourage women to apply for the position.

The application, including attachments, should be written in Swedish or English. You apply via our e-recruitment system Varbi. Log in and apply via the button at the bottom of the page. The deadline for applications is September 30, 2019.

Further information
Further information is provided by Professor Jun Yu, +46-(0)90-786 51 27, and Assistant Professor Armin Eftekhari, You can also contact the Head of department Åke Brännström for additional questions at

We look forward to receiving your application!

Research at the Department of Mathematics and Mathematical Statistics is conducted within mathematics, mathematical statistics and computational science. Important cooperation partners include the Faculty of Science and Technology, the Faculty of Medicine, Umeå School of Business and Economics, Umeå School of Sport Sciences, the University Hospital, the Faculty of Forest Sciences at the Swedish University of Agricultural Sciences, as well as public authorities and industry. We provide education at all levels with a particular focus on civil engineering programs.

For more information see

Type of employment Temporary position
Contract type Full time
First day of employment Fall 2019 or according to agreement
Salary Monthly salary
Number of positions 1
Full-time equivalent 100%
City Umeå
County Västerbottens län
Country Sweden
Reference number AN 2.2.1-1209-19
  • Jun Yu,, +46 90 7865127
  • Armin Eftekhari,
  • Åke Brännström,, 090-7867862
Union representative
  • SACO, +46 90 786 53 65
  • SEKO, +46 90 786 52 96
  • ST, +46 90 786 54 31
Published 21.Aug.2019
Last application date 30.Sep.2019 11:59 PM CEST

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