Doctoral Candidate 7 in BRIDGE-AI

• Perform top class research in the field of medical image processing

• Find creative solutions for challenging image processing questions

• Develop novel quantitative image processing pipelines with a special focus on deep learning (programming in Python)

• Develop and execute validation of novel algorithms

• Stay informed on the developments and trends in the field of medical image processing

• Discuss the progress and intermediate results of research projects both internally and externally

• Present your work to an international multi-disciplinary community

• Write several high-quality scientific articles related to the research project and publish them in peer-reviewed journals

• Enroll as a PhD student at Aalto University, Espoo, Finland

For this project, we foresee secondments to:

• prof. Koen Van Leemput  (1 month) at Aalto University (Finland)  

• Prof. Martin Reuter (3 months) at Deutsches Zentrum Für Neurodegenerative Erkrankungen (DZNE) (Germany)

PhD Project description

PhD project description: A significant challenge in large-scale neuroimaging studies is the robust segmentation and analysis of images acquired from different centers, each with unique imaging protocols and scanner platforms. This variability introduces domain shifts that can degrade the performance of machine learning models, limiting their generalizability. To overcome this, methods that can learn representations of brain MRI scans that are invariant to scanner-specific characteristics but retain crucial anatomical and pathological information are needed. Self-supervised learning (SSL) presents a promising avenue to achieve this by pre-training models on vast, unlabelled datasets to develop foundational representations that can be effectively adapted to a wide range of subsequent, or downstream clinical prediction tasks across different diseases. 

This PhD project will focus on developing novel self-supervised learning methods to create generalizable representations from heterogeneous brain MRI scans. The primary objective is to leverage SSL by pre-training a model on thousands of diverse MRI scans, enabling it to serve as a powerful auxiliary for various downstream tasks such as brain age prediction, lesion segmentation, and the prognosis of disease evolution. A key component of this project will be a rigorous evaluation of different SSL techniques and pretext tasks to establish their benefits and formulate standardization guidelines for their application. The expected results include a robust, pre-trained model ready for application in diverse clinical tasks and a comprehensive set of guidelines for applying SSL in medical imaging. The utility of this model will be demonstrated through a secondment at DZNE, where it will be applied to an in-house dataset to showcase improved training efficiency and performance against existing benchmarks. 

Minimum qualifications

• Your profile aligns with the general requirements and eligibility criteria of the MSCA BRIDGE-AI project (see https://bridge-ai-)

• You have a Master's degree in (biomedical) engineering, physics, computer science or related fields (or will have by the time of your appointment)

• Experience in scientific programming (preferably Python)

• Work accurately and independently as well as within a multidisciplinary team

• Eager to learn, entrepreneurial, multicultural attitude, results-oriented

• Fit with our values and culture

• Fluent in English

Nice-to-have qualifications

• Background in magnetic resonance imaging (MRI) and/or scientific computingExperience in Medical Image Computing (e.g. master thesis) and/or Deep learning knowledge

• Knowledge of other languages

• The selected candidate will be employed by icometrix for 36 months on the MSCA-DN BRIDGE-AI project.

• Doctoral candidates are offered a competitive remuneration based on the MSCA allowances and the regulations of the organisation. icometrix will receive the following EU-grant to recruit a Doctoral Candidate (DC): monthly Living Allowance € 4.010; monthly Mobility Allowance € 710; and monthly Family Allowance € 660 (only if applicable). Please note that the final monthly, gross salary will result from deducting (from the mentioned amounts) all compulsory national labour taxes (social security, etc.) to be borne by the employer. Moreover, funding is available for technical and personal skills training and participation in international research events.

• Expected start date: between April and September 2026. We encourage last-year master students who will graduate by this time to already apply.
  More information is available on the BRIDGE-AI website: https://bridge-ai- 

For additional information about this specific research project, contact the main supervisor:

Dr. Simon Van Eyndhoven -

HOW TO APPLY: Please do not apply directly on this website. Go to the BRIDGE-AI website instead and follow the instructions to apply for this position: https://bridge-ai- 

• This job as a PDF file