PhD Position in Umea University Sweden

PhD student in Computational Science and Engineering with a focus on machine learning – Umea, Sweden


Department of Computing science invites applications for a PhD student in Computational Science and Engineering with a focus on machine learning at Umeå University, Sweden, 2022

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General Info

Position: PhD student
No. of Positions: 1
Research Field: ,
Deadline to Apply: Expired
Joining Date: June 01, 2022
Contract Period: 4 Years
Salary: 'According to standard norms'

Department of Computing science

Umeå University (UMU), Sweden


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Qualification Details

The general admission requirements for doctoral studies are a second-cycle level degree, or completed course requirements of at least 240 ECTS credits, of which at least 60 ECTS credits are at second-cycle level, or have an equivalent education from abroad, or equivalent qualifications.

To fulfill the specific entry requirements for doctoral studies in computational science and engineering, the applicant is required to have completed at least 90 ECTS credits in relevant fields for computational science and engineering (mathematics, mathematical statistics, and computing science), of which at least 30 ECTS credits are at second-cycle level. Applicants who otherwise have acquired skills that are deemed equivalent are also eligible.

Candidates are expected to have very good knowledge in machine learning, deep learning, computer vision, and image analysis. Experience in working with medical images is a merit. A very good command of the English language, both written and spoken, is a key requirement.

Important personal qualities are the ability to work on your own as well as together with others, to be creative, and to have a will to actively develop yourself to become a competent researcher.

Responsibilities/Job Description

Methods for automatic segmentation based on the latest development in artificial intelligence (AI) have gone through a rapid development during the last few years, and work remarkably well. Such methods can be used in radiation therapy to automatically segment, in medical images, the area that should be treated and the areas where it is important to avoid radiation exposure to not hurt sensitive organs or tissues. These methods are however exclusively developed for adult patients, whereas the development of corresponding AI tools for paediatric patients has lagged severely.

The purpose of this project is therefore to develop and evaluate modern AI methods for automatic segmentation in medical images, which are based on deep learning and in particular on deep convolutional neural networks, and that also work well for paediatric cancer patients, where a problem is that of heterogeneous and imbalanced data.

The doctoral student position is offered within the machine learning project ”Automated radiotherapy for paediatric oncology patients and the challenge of small datasets” that is financed by the Swedish Childhood Cancer Fund. The project is a collaboration between researchers at the Department of Computing Science and the Department of Radiation Sciences, both at Umeå University. The project is led by Tommy Löfstedt, docent and associate professor at the Department of Computing Science, Umeå University.

How to Apply?

Application Method: Online Application
Ref. No.: AN 2.2.1-587-22

Application Procedure

  • A cover letter including a description of your research interests, your reasons to apply for the position, and your contact information
  • A curriculum vitae (CV)
  • Certified copies of degree certificates, including documentation of completed academic courses and obtained grades
  • Copies of completed BSc and/or MSc theses and other relevant publications, if any
  • Contact information to three references
  • Documentation and description of other relevant experiences or competences.

The application must be written in English or Swedish. Attached documents in other languages should be translated. Attached documents must be in pdf format.

About the Department/Section/Group

The Department of Computing science has been growing rapidly in recent years where focus on an inclusive and bottom-up driven environment are key elements in our sustainable growth. The 50 Doctoral students within the department consists of a diverse group from different nationalities, background and fields. If you work as a Doctoral student with us you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions. See more information at:

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Contact details

For additional information, please contact associate professor Tommy Löfstedt ([email protected]).

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