PhD Deep Learning Medical Imaging Gistrup - Anomaly Detection

At the Faculty of Medicine, Department of Health Science and Technology, one or more PhD stipends in Unsupervised Learning for Medical Image Analysis are available for appointment from November 1, 2026, or as soon as possible thereafter. The appointment is for a period of three years. The successful candidate will be enrolled in the PhD programme in Biomedical Engineering and Neuroscience at the Faculty of Medicine.

Who we are

As a PhD fellow at HST, you will become part of an international research community with access to experienced supervisors, modern research facilities and strong interdisciplinary collaboration. You will work alongside researchers from a wide range of disciplines and engage with hospitals, industry and public-sector partners to address some of society’s most important health challenges.

The department is home to approximately 180 academic staff members, around 100 PhD fellows and more than 1,700 students. We offer a stimulating research environment that encourages scientific curiosity, collaboration and professional development.

The Faculty of Medicine is committed to preparing PhD graduates for diverse career paths. Throughout your PhD, you will develop advanced research competencies alongside transferable skills that prepare you for careers in academia, industry and the public sector. Towards the end of your PhD, you will be offered dedicated career development support to help you plan your next career step.

At HST, our values – Share. Care. Dare. – shape the way we collaborate, support one another and create knowledge with societal impact.

Learn more about the department on our website and in our research portal.

Opportunistic screening envisions a scenario in which all acquired medical images are studied in minute detail to detect the early signs of any disease. When combined with AI tools, this has the potential to

• detect diseases earlier, giving the best chance of improving patient outcomes
• reduce the burden placed on radiologists and democratize diagnostic expertise to more healthcare institutions
• maximize the utility of collected radiological data

One of the major barriers to achieving this, is figuring out how to learn useful patterns in the data without relying on exhaustive labelling. For example, in the anomaly detection setting, we often assume that only healthy images are available during training. Using this, we wish to learn the appearance and structure of healthy anatomy and detect any deviation from the healthy distribution. But in the absence of labels, how should we direct the model to learn relevant features, and how can we determine which features are relevant? These questions are at the core of anomaly detection and despite being a well-established field of research, these are still very much open problems.

To this end, we are looking for candidates interested in developing new machine learning methods for medical image analysis, with a particular focus in anomaly detection and unsupervised learning. In this position, you will have the chance to explore basic machine learning research as well as more applied projects. While most of the work will involve technical machine learning research, you will also have access to clinical experts and will be expected to collaborate closely with other researchers in the Faculty of Medicine and at Aalborg University Hospital.

Applicants should have:

• A strong technical background in machine learning, computing, data science, biomedical engineering, or a related field at the level of a master degree
• Programming skills (Python) and experience with common machine learning platforms
• Experience with deep learning, computer vision, medical image analysis or unsupervised learning is an advantage.
• English language skills, both written and spoken

Qualification requirements

How to apply

• Application, stating reasons for applying and qualifications in relation to the position
• Curriculum Vitae (CV)
• Diplomas (bachelor’s and master’s degree diploma, including grades)
• Research statement (max 1 page excluding references), briefly describing a proposed research project. Include a problem description, a brief summary of existing solutions, and your proposed approach/topic that you would like to investigate.
• References (optional)
• Other relevant documents

The application must be submitted via Aalborg University’s recruitment system, which can be accessed under the job advertisement on Aalborg University’s website.

Do you have any questions?     

Further information  

The assessment of candidates for the position will be carried out by qualified experts. Shortlisting will be applied. This means that after the application deadline, the head of the department, with the assistance of the hiring committee, will select the applicants to be assessed. All applicants will be informed whether they have been shortlisted for assessment or not.

The hiring process at Aalborg University may include a risk assessment as a tool to identify potential risks associated with new hires, ensuring the safety, compliance, and integrity of the workplace.

Read more about The Doctoral School in Medicine, Biomedical Science and Technology

Salary and terms of employment

You can find information about the salary and salary structure for the position in the Salary Overview for Recruitment – Scientific positions (VIP). Please refer to the introductory section of the page and the section entitled “PhD Fellow”.

There is a mutual probationary period of 3 months for the position.

Aalborg University

Denne stilling er publiceret af Aalborg Universitet via jobnet.dk