Umeå University is hiring two PhD students in radiation physics with a research profile in deep learning and medical image analysis. The positions are at the Department of Radiation Sciences and covers four years of full-time studies. Applications should be submitted no later than February 26, 2018.
The purpose of these research projects is to develop advanced methods to simplify and automate the workflow in radiation treatment. The goal is to make current parts of the workflow more effective and reduce the variation in for example the delineation of organs at risk. The assumption is that this will improve the patients’ situations and increase the patient throughput in radiation treatment. The project may include automatic segmentation of tumours and organs at risk, automatic conversion of MRI images to CT images for use in radiation dose calculations, or automatic patient motion correction.
The most promising methodology for such applications are in the field of artificial intelligence and machine learning using deep neural networks, or more specifically, deep convolutional neural networks. These machine learning methods have previously been successfully used in other areas of medical image analysis, and in pilot studies with similar applications.
Machine learning methods are often validated on idealized data that was collected for a specific purpose. In this project, the developed methods and methodologies will be validated in the clinic on real and on-line data. In this part of the project, the robustness and generalizability of the methods is of uttermost importance. Outliers and other deviations must be investigated, as must how well the data flows function in practice, and how to perform quality control on the output from the developed methods. These investigations will be performed in close collaboration with clinically working physicists, medical doctors and nurses.
The goal is to construct software infrastructure and base lines for this methodology, and to further develop the methods in order to offer world-learning solutions to the Swedish health care.
Applicants must hold a University degree of at least 240 credits (ECTS), including 60 credits at the advanced level, or equivalent levels of education otherwise acquired in Sweden or abroad.
The applicants should have a background in computer science, physics, mathematics, mathematical statistics, or related areas. Practical experience in and an understanding of machine learning, programming experience, and experience in image analysis is a merit, as is experience of health care, and of research projects related to health care. Practical and theoretical experience in machine learning, data science or related areas is a strong merit.
The applicants must be able to work both independently and as part of a team with diverse expertise, and much focus will be given to the candidate’s potential collaborative skills. A requirement is that the applicants are skilled in both oral and written English.
Depending on the backgrounds, experiences and interests of the applicants, these two projects may be either split in one that leans toward machine learning and one that leans toward clinical research. Alternatively, they may be two parallel projects containing both components.
The applicant must be skilled in both oral and written communication in English, and this is evaluated by a presentation of the research plan in English for the supervisors and the postgraduate studies board of the department.
The application should be written in Swedish or English and contain:
Your application must be registered in the Umeå University e-recruitment system no later than 2018-02-26. Reference number: AN 2.2.1-171-18
More information regarding these positions can be sought from Tommy Löfstedt (PhD), email@example.com and Joakim Jonsson (PhD), firstname.lastname@example.org.
We are looking forward to receive your application!
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