Postdoctoral position in deep learning based image regression from indirect noisy observations

Updated: 8 months ago
Deadline: 13 Dec 2021

Published: 2021-11-08

The deadline for applying to this position has passed.

Uppsala University is a comprehensive research-intensive university with a strong international standing. Our ultimate goal is to conduct education and research of the highest quality and relevance to make a long-term difference in society. Our most important assets are all the individuals whose curiosity and dedication make Uppsala University one of Sweden’s most exciting workplaces. Uppsala University has over 45,000 students, more than 7,000 employees and a turnover of around SEK 7 billion.

At the Division of Systems and Control, we develop both theory and concrete tools for learning, reasoning, and acting based on data. An overarching goal is for both humans and machines to better understand the complexity of the real world. Probabilistic models form a central part of our research, allowing us to systematically represent and cope with the uncertainty inherent in most data.  Data and learning algorithms are also important components of our research. It remains a major challenge to develop efficient and accurate learning algorithms capable of handling high-dimensional models, data rich applications, complex model structures, and diverse data sources that arise in many of the data analysis problems that we are currently facing.

We have a wide network of strong international collaborators all around the world, for example at the University of Cambridge, University of Oxford, University of British Columbia, University of Sydney, University of Newcastle and Aalto University. There are also ample opportunities for collaborations with other leading machine learning groups in Sweden and Europe, through our affiliations with WASP ( ) and the ELLIS society ( ), respectively.

Read more about our benefits and what it is like to work at Uppsala University

We offer a two-year postdoctoral fellowship based on a grant from Kjell and Märta Beijer Foundation and the Tandem Forest Values programme at the Royal Swedish Academy of Agriculture and Forestry.

The position includes research into theory and development of deep learning algorithms for computer vision regression tasks in an inverse problems setting, meaning that input data represents indirect noisy observation of the image. One aim is to extend energy-based models for deep probabilistic regression to such a setting, e.g., by including a handcrafted physics model for generating data from an image. Work will be spearheaded by the need to detect and locate interior imperfections (cracks, knots, metallic inserts, etc.) of logs from noisy X-ray projection data. This application is part of a collaboration with a larger international project supported by the Academy of Finland involving researchers at LUT-University and University of Oulu with an overall goal of developing methods for image guided optimization of the sawline in processing of forest logs.

The research will be pursued at the Department of Information Technology at Uppsala University. As a postdoctoral fellow, you will benefit from the strong research environments at Uppsala University in machine learning.

To qualify for an employment as a postdoctor you must have a PhD degree or a foreign degree equivalent to a PhD degree in mathematics, signal processing, computer science, or computational physics/engineering. The PhD degree must have been obtained no more than three years prior to the application deadline. The three year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc. The candidate must have a strong background from machine learning or signal/image processing with experience from software development in scientific computing or machine learning using Python and/or C/C++. Finally, a successful candidate must be strongly motivated and have the capability to work independently as well as in collaboration with members of the research group.

Additional qualifications
Experience from tomographic image reconstruction is highly desirable. An additional advantage is a research track record with publications at leading conferences in machine learning. As a person, you are creative, thorough and have a structured approach. When selecting among the applicants we will assess their ability to independently drive their work forward, to collaborate with others, to have a professional approach and to analyze and work with complex problems. Great emphasis will be placed on personal characteristics and personal suitability.

Application: The application must contain:

  • A curriculum vitae (CV),
  • A copy of relevant grade documents (translated into Swedish or English),
  • A list of publications
  • Up to five selected publications in electronic format
  • A research statement describing your past and current research (max 1 page) and a proposal for future activities (max 1 page).
  • Contact information for two references.
  • A cover letter briefly describing your motivation for applying for this position and the earliest possible employment date (max 1 page).
  • About the employment
    The employment is a temporary position according to central collective agreement. Scope of employment 100 %. Starting date upon agreement, but preferably no later than 31 March 2022. Placement: Uppsala

    For further information about the position, please contact: Professor Ozan Öktem (phone: +46-733-52 2185, e-mail: or Professor Thomas Schön (phone: +46-18-471 2594, e-mail:

    Please submit your applicationby 13 December 2021, UFV-PA 2021/4202.

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    Submit your application through Uppsala University's recruitment system.

    Placement: Department of Information Technology

    Type of employment: Full time , Temporary position longer than 6 months

    Pay: Fixed salary

    Number of positions: 1

    Working hours: 100%

    Town: Uppsala

    County: Uppsala län

    Country: Sweden

    Union representative: Seko Universitetsklubben

    Number of reference: UFV-PA 2021/4202

    Last application date: 2021-12-13

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