Open PhD positions

The BRIDGE-AI doctoral network is employing 15 ambitious doctoral candidates at various locations in Europe (Finland, the Netherlands, Belgium, Germany, Switzerland and Austria). We are currently inviting applications for the last 3 open PhD positions, located in Germany and Finland (see below). The aim of the BRIDGE-AI network is to develop new and trustworthy AI methods for longitudinal neuroimage analysis, helping clinical experts assess temporal changes in patients with chronic brain disorders. More information about the project is available from the BRIDGE-AI website.

Employer: German Center for Neurodegenerative Diseases (DZNE)  

PhD enrollment: University Bonn 

Main supervisor: Prof. Dr. Martin Reuter 

PhD project description:  

Are you passionate about advancing neuroscience and clinical applications through cutting-edge AI technology? Do you want to contribute to tools that can transform how we understand brain function, study neurological diseases, and evaluate large-scale population studies? Join us to develop innovative AI methods for cortical surface modeling – an essential foundation for modern neuroimaging research. 

Why This Matters: The cerebral cortex, with its intricate folds and complex structure, holds key insights into brain health and disease. Accurate modeling and analysis of the cortical surface enable breakthroughs in understanding brain development, detecting abnormalities, and tracking disease progression. Your work will help build powerful AI tools that can improve diagnosis, guide treatment strategies, and push the boundaries of neuroscience research. 

In this PhD project, you will: 

  • Develop advanced AI methods to segment brain cortical atlases based on surface data, moving beyond traditional approaches. 
  • Create algorithms to detect anomalies on the cortical surface, such as Focal Cortical Dysplasia (FCD), aiding early diagnosis of neurological conditions. 
  • Innovate deep learning solutions for longitudinal surface extraction, tracking brain changes over time with unprecedented accuracy. 
  • Implement state-of-the-art deep learning techniques for cross-subject and cross-time surface registration. 
  • Thoroughly validate your methods to ensure broad applicability in neuroimaging workflows. 

Your contributions will elevate the accuracy, reliability, and generalizability of cortical surface analysis, setting new standards for the field. They will generate impact through their possible integration into widely used neuroimaging pipelines such as FastSurfer and FreeSurfer.  

What We Offer: We are the developers of FastSurfer and co-developers of FreeSurfer, world-leading open-source neuroimage analysis pipelines. We are experts in developing advanced deep-learning methods for medical image processing. At our lab you will have access to excellent supervision and to a powerful computational environment, including a high-performance cluster with close to one hundred NVIDIA 32GB GPUs and four dedicated servers reserved exclusively for our group, each with 8 GPUs (32-40GB RAM each). Furthermore, we offer research stays at world-renowned institutions, including the Martinos Center at MGH and Harvard Medical School, Boston USA, as well as Icometrix, Belgium. We provide a dynamic, collaborative research environment with mentorship from experts in AI, neuroimaging, and clinical neuroscience. 

Who You Are: 

  • You hold a strong background in computer science, biomedical engineering, applied mathematics, or a related field. 
  • You have experience in machine learning and deep learning with applications in medical imaging or neuroscience. 
  • You are motivated to work on impactful research that bridges AI development with real-world neurological applications. 
  • You possess solid programming skills (Python and PyTorch) and optimally have worked with geometric representations, such as triangle meshes. 
  • Experience with brain MRI data, or longitudinal analysis is a plus but not mandatory. 

If you’re eager to push the frontiers of AI in neuroscience and contribute to transformative research with world-class collaborators and resources, we want to hear from you! 

Foreseen secondments: For this PhD project, research visits are foreseen to:  

  • Dr. Dirk Smeets (1 month) at Icometrix (Leuven, Belgium) – industry leader on advanced methods for medical image analysis. 
Apply now!

Employer: Aalto University, Espoo, Finland

PhD enrollment: Aalto University, Espoo, Finland

Main supervisor: prof. Koen Van Leemput

PhD project description: The assessment of whether a fetus is at the expected development stage is currently performed in a rather primitive fashion in the clinic: clinicians manually perform 2D measurements from fetal brain MRI scans, and compare those to normative growth curves to see whether development is delayed. In this PhD project, you will significantly advance this state of affairs by developing new probabilistic generative models of the developing neuroanatomy across gestational ages, and use those models to accurately estimate the gestational age from fetal MRI scans.

This is a highly relevant but also very ambitious project: In order to be fully clinically useful, the methods and tools that you will develop should work “out-of-the-box” on data that is acquired with different scanners, image resolutions, reconstruction algorithms and acquisition protocols. Furthermore, your models should encode the cause-effect relationship between gestational age and the acquired MRI scans, so that the obtained predictions are inherently interpretable for clinicians (for instance, by generating counterfactuals simulating a particular fetus’s neuroanatomy at different gestational ages). Once you are confident in the performance of your methods and tools, you should also apply them to large clinical datasets of both normal and abnormal brain development.

Your profile: This position requires strong skills in both math (e.g., linear algebra, Bayesian statistics) and coding (e.g., Python/NumPy, C++). Prior experience with medical imaging is not required.

Foreseen secondments: For this PhD project, research visits are foreseen to:

Apply now!

Employer: Aalto University, Espoo, Finland

PhD enrollment: Aalto University, Espoo, Finland

Main supervisors: prof. Koen Van Leemput & Dr. Richard McKinley (Bern, Switzerland)

Clinical Problem: Among patients who present with a first seizure, approximately 45% will have a further seizure within the next five years. Identifying which patients are likely to have recurrent seizures is therefore vitally important, since early treatment can dramatically improve the lives of patients with epilepsy and spare them from further neurological deficits brought on by repeated seizures.

Structural brain lesions are the most common cause of epileptic seizures, but the mere presence of a lesion is not predictive of epilepsy: the size and location of the lesion, and its proximity to the grey matter, are critical factors. Similarly, while loss of grey matter is seen in all epilepsy patients over time, specific patterns of grey-matter atrophy can be associated to different epilepsy subtypes. Identifying these patterns in a timely manner has the potential to drive patient treatment.

Your solution: In this PhD project you will develop methods to link patterns of grey- and white-matter alterations to epilepsy subtypes and to distinguish epilepsy patients from those with epilepsy mimics. You will benchmark and extend existing segmentation methods for quantifying white matter lesions and grey matter atrophy, and establish baselines for linking alterations and disease, such as lesion-symptom mapping and structural covariance networks. You will then explore how generative modeling can be harnessed to translate patterns of brain alteration into low-dimensional latent codes which capture the essence of a patient’s deviation from normal brain appearance, and harness these codes to better estimate a patient’s risk of receiving a definitive epilepsy diagnosis. The methods developed will be based on data from, and have direct application in, ongoing studies at the University Hospital Bern and across Switzerland, which aim to identify patients at high risk of developing epilepsy after a first seizure, and to study the effects of epilepsy over time on the structure, function and metabolism of the brain.

Foreseen secondments: For this PhD project, research visits are foreseen to:

  • Prof Roland Wiest & Dr. Richard McKinley, Inselspital, University Hospital Bern, Switzerland (10 months across 3 visits)
  • Dr Ankur Sharma, Bayer (Berlin, Germany) (2 months)
Apply now!

We offer:

  • A unique and stimulating environment to work on cutting-edge AI method development for computational brain imaging, with ample opportunities for collaborating closely with expert clinicians and world-leading companies in the medical imaging domain. You will be co-supervised by a multidisciplinary team, receiving valuable input from industry, academia, and hospitals alike.
  • A dedicated PhD training program with high-profile invited lecturers, combining both science-based and transferable skills training, with attention to your future career development. All BRIDGE-AI’s doctoral candidates will convene on regular intervals for training events organized in Helsinki (FI), Leuven (BE), Lausanne (CH), Bern (CH), Vienna (AT) and Eindhoven (NL).
  • Access to large collections of clinical neuroimaging data and expert guidance (including through extensive international research visits) for evaluating the true clinical relevance and impact of the AI tools you will be developing.
  • Full-time employment on the BRIDGE-AI project with competitive remuneration for three years. Depending on the host organization, subsequent funding needed to finalize your PhD thesis may be available.

Your profile:

  • A MSc degree in physics, computer science, electrical engineering or a similar degree. Candidates who expect to obtain their MSc degree in the near future are also welcome to apply.
  • A strong interest in both new AI method development and the real-world clinical context in which AI methods need to operate. Able to work in an interdisciplinary team and interested in collaborating with clinical partners.
  • A high level of perseverance.
  • Strong programming skills in e.g., Python/NumPy, PyTorch or C++.
  • Excellent communication skills in English (both oral and written).
  • An eagerness to travel within Europe.

Other prerequisites:

  • Applicants can be of any nationality, but must not have resided or carried out their main activity (work, studies, etc.) in the country of employment for more than 12 months in the 36 months immediately before their date of recruitment. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.
  • At the date of the recruitment, applicants can not already be in possession of a doctoral degree. Researchers who have successfully defended their doctoral thesis but who have not yet formally been awarded the doctoral degree are also not eligible.
  • Successful applicants will need to enroll in a doctoral program at the PhD awarding institution associated with each position. Therefore, eligibility requirements (e.g., relevant MSc degree, language proficiency, …) for the relevant institution need to be fulfilled. Please see the FAQ page for institution-specific requirements.

To apply:

  • Applications can only be submitted through Aalto University’s online job platform until 30 June 2026 at 23:59 Finnish time (GMT +2), or until all positions have been filled (whichever comes first).
  • Screening and filling of the positions will start as soon as applications are received. Therefore, it is recommended to apply as early as possible.
  • We strive to ensure diversity and gender equality in the BRIDGE-AI network through an open, transparent, and merit-based recruitment. Women and others underrepresented in the field of computational neuroimaging are particularly encouraged to apply.
  • In order to apply, you should include: (1) a cover letter explaining your motivation for applying; (2) your CV; (3) relevant transcripts of studies and certificates of your degrees; and (4) the names and contact information of at least two professional references who may be contacted regarding your application. You should indicate which position(s) you are applying to (maximum 3), and indicate the order of preference within the selected positions (1 = highest preference).
  • We reserve the right to leave positions open, to extend the application period, and to reopen the application process.
  • For more information about the application and selection procedure, please see the FAQ page.
Apply now!

FAQ page with more information

Recruitment privacy notice