PhD fellowship in Medical Image Analysis

PhD Project in early detection of lung disease from CT in patients treated for breast cancer

Department of Computer Science
Faculty of SCIENCE

University of Copenhagen

The IMAGE section at the Department of Computer Science invites applicants for a PhD fellowship in early detection of chronic lung disease from CT in patients treated for breast cancer. The project is part of the large research project Artificial Intelligence for early detection of non-communicable disease risk in people with breast cancer (ARTILLERY), which is a EU Horizon financed project involving universities, hospitals, and companies across EU.

Start date is expected to be 1 July 2024 or as soon as possible thereafter.

The project

Chronic obstructive lung disease (COPD) is a disease which affects lung function. It can severely affect quality of life and is one of the most common causes of death. Lung and airway abnormalities associated with COPD are visible on computed tomography (CT) scans. CT scans are, however, not done as part of normal COPD diagnosis, but in comparison to the simpler lung function tests done as part of normal diagnostics, information from such scans can be used to provide better insights into the anatomy and pathology in the individual case and can be used to provide early detection. Cancer treatments, such as radiotherapy, often involve CT images, and such treatments therefore offer a unique opportunity to quantify signs of COPD and use this information to provide early guidance and help to patients that is not otherwise available.

The project aims to accomplish this by developing and evaluating machine learning based methods for quantifying COPD in CT scans of breast cancer patients. To quantify the degree to which such methods can detect early signs and predict risk of the disease. This may involve segmentation and analysis of airway structures, emphysema and other abnormalities associated with COPD and the development of machine learning based risk prediction models.

The project will rely on close collaboration with the Department of Oncology, Rigshospitalet as well as the other EU hospitals involved in the project where the data will come from and the necessary medical expertise is found. The collaboration with the Department of Oncology at Rigshospitalet is already established, with multiple PhD students and the co-supervisor involved in shared positions and the PhD student is therefore expected to benefit from existing knowledge and network.

Who are we looking for?

We are looking for candidates with a background in computer science, physics, engineering, mathematics, or similar. To be eligible to apply for these positions, applicants need to have or be about to obtain an MSc degree in one of these fields (education level options are discussed further below). In addition, the ideal candidate might have

• professional qualifications relevant to the PhD project
• experience in medical imaging, image analysis, and/or machine learning / deep learning
• a wish to apply advanced computer science and machine learning techniques in medicine
• a creative, solution oriented mindset and the ability to work both independently and in research teams
• The ability to communicate and work with researchers from various clinical and technical fields
• relevant publications
• relevant work experience
• other relevant professional activities
• good language skills, the group is international and fluency in spoken and written English is a requirement

Our group and research – and what do we offer?

The project will be carried out at the Image Analysis, Computational Modelling, and Geometry (IMAGE) section at the Department of Computer Science, Faculty of Science, University of Copenhagen. The research group is relatively large, diverse and internationally renowned, consisting of more than 20 PhDs and Postdocs, many of whom work on medical imaging.

The project will be supervised by Marleen de Bruijne, Professor of AI in Medical Image Analysis at the Department of Computer Science, University of Copenhagen and at Erasmus MC, Rotterdam, The Netherlands, and by  Associate Professor Jens Petersen, who is in a shared position between the IMAGE section at the Department of Computer Science and the Department of Oncology at Rigshospitalet.

The PhD programme

A three year full-time study within the framework of the regular PhD programme (5+3 scheme), if you already have an education equivalent to a relevant Danish master’s degree.

Getting into a position on the regular PhD programme

Qualifications needed for the regular programme
To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. computer science, physics, engineering, mathematics. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database .

Terms of employment in the regular programme
Employment as a PhD fellow is full time and for a maximum of 3 years.

Employment is conditional upon your successful enrolment as a PhD student at the PhD School at the Faculty of SCIENCE, University of Copenhagen. This requires submission and acceptance of an application for the specific project formulated by the applicant.

The terms of employment and salary are in accordance with the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State (AC). The position is covered by the Protocol on Job Structure.

Responsibilities and tasks in the programme

• Carry through an independent research project under supervision

• Complete PhD courses corresponding to approx. 30 ECTS.
• Participate in active research environments, including a stay at another research institution, preferably abroad
• Teaching and knowledge dissemination activities
• Write scientific papers aimed at high-impact journals
• Write and defend a PhD thesis on the basis of your project

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Application and Assessment Procedure

Your application including all attachments must be in English and submitted electronically by clicking APPLY NOW below.

Please include: 

Application deadline:

The deadline for applications is 10 March 2024, 23:59 GMT+1.

We reserve the right not to consider material received after the deadline, and not to consider applications that do not live up to the abovementioned requirements.

The further process
After deadline, a number of applicants will be selected for academic assessment by an unbiased expert assessor. You are notified, whether you will be passed for assessment.

The assessor will assess the qualifications and experience of the shortlisted applicants with respect to the above mentioned research area, techniques, skills and other requirements. The assessor will conclude whether each applicant is qualified. The assessed applicants will have the opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/ .

Interviews with selected candidates are expected to be held during May 2024.

Questions
For specific information about the PhD fellowship, please write both supervisor Marleen de Bruijne at email: [email protected] and co-supervisor Jens Petersen at email: [email protected] .

General information about PhD study at the Faculty of SCIENCE is available at the PhD School’s website: https://www.science.ku.dk/phd/ .

The University of Copenhagen wishes to reflect the surrounding community and invites all regardless of personal background to apply for the position. 

Part of the International Alliance of Research Universities (IARU), and among Europe’s top-ranking universities, the University of Copenhagen promotes research and teaching of the highest international standard. Rich in tradition and modern in outlook, the University gives students and staff the opportunity to cultivate their talent in an ambitious and informal environment. An effective organisation – with good working conditions and a collaborative work culture – creates the ideal framework for a successful academic career.