Doctoral Candidate 4 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  (5 months) at Aalto University (Finland)  

PhD Project description

Effective clinical management of Multiple Sclerosis (MS) relies on longitudinal Magnetic Resonance Imaging (MRI) to track disease progression through measures of brain atrophy and white matter lesion evolution. However, the diagnostic precision of this approach is significantly undermined by technical variability between MR scanners and a lack of data harmonization. This challenge is exacerbated in real-world clinical settings by the heterogeneity of acquired images—varying in contrasts, voxel resolutions, and scanner-specific properties—often occurring within a single patient's longitudinal data. These inconsistencies introduce scanner-related biases that can obscure or mimic genuine pathological changes, creating a critical and unresolved barrier to reliable, long-term patient monitoring. 

This PhD project will directly address the problem of data heterogeneity by developing novel deep learning (DL) methodologies for the harmonization and analysis of longitudinal MRI data in MS. The primary objective is to create image-level harmonization techniques that enable robust, reliable tracking of annualized brain volume loss and changes in white matter lesion volume. To achieve this, the research will investigate promising DL-based approaches, such as adversarial learning, for targeted scanner-bias reduction. A key component of this work will be the creation of a comprehensive validation framework using real-world clinical brain images to benchmark MS lesion evolution estimation. The expected outcomes are robust modules for both brain atrophy computation and lesion evolution analysis, designed for implementation within the longitudinal icobrain ms software, while also advancing knowledge on uncertainty estimation and the explainability of DL models in clinical applications. 

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. Diana Sima -

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