Postdoctoral position in deep learning based sparse view tomography
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 54,000 students, more than 7,500 employees and a turnover of around SEK 8 billion.
The Department of Information Technology has a leading position in research and all levels of higher education. Today the department has 300 employees, including 120 academic staff and 110 full-time PhD students. The Department comprises research and education in a spectrum of areas within Scientific Computing, Image Analysis, Computer Science, and Information Technology. More than 4000 students take one or several courses offered by the Department each year.
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.
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 tomographic image reconstruction, meaning that input is noisy sparse view tomographic data of an object. One aim is to merge deep probabilistic regression with handcrafted physics models 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 sparse view tomographic 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. It is also part of a recently initiated collaboration with researchers at the Wood Science and Engineering at Luleå University of Technology.
The research will be pursued at the Department of Information Technology at Uppsala University jointly with the Department of Mathematics at KTH. As a postdoctoral fellow, you will benefit from the strong research environments at Uppsala University in machine learning.
PhD degree in in mathematics, signal processing, computer science, or computational physics/engineering or a foreign degree equivalent to a PhD degree in in mathematics, signal processing, computer science, or computational physics/engineering. The degree needs to be obtained by the time of the decision of employment. Those who have obtained a PhD degree three years prior to the application deadline are primarily considered for the employment. The starting point of the three-year frame period is the application deadline. Due to special circumstances, the degree may have been obtained earlier. 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.
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.
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 of 2 years according to central collective agreement. Scope of employment 100 %. Starting date upon agreement, but preferably no later than January 1, 2023. Placement: Uppsala.
For further information about the position, please contact: Professor Ozan Öktem (phone: +46-733-52 2185, e-mail: email@example.com) or Professor Thomas Schön (phone: +46-18-471 2594, e-mail: firstname.lastname@example.org).
Please submit your application by 7 October 2022, UFV-PA 2022/3168.
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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%
County: Uppsala län
Number of reference: UFV-PA 2022/3168
Last application date: 2022-10-07
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