The Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI) at Danube Private University, led by Ramona Woitek, Univ.-Prof. Dr. was founded in 2022. It currently comprises multiple multidisciplinary researchers focusing on the computer-based analysis of radiological and histological images. Their work includes applying machine learning and artificial intelligence for tasks such as predicting treatment responses in different types of cancers and automated image segmentation.


This position is offered within the framework of a WWTF (Vienna Science and Technology Fund)-funded project, entitled “LymphoidStructureMiner: AI-based exploration of the immunological contexture of lymphoid structures in translational research”. The project is a joint effort between the Medical University of Vienna, with Diana Mechtcheriakova, Assoc.-Prof. Priv. Doz. Dr. Dipl.-Ing. (Principal Investigator), Anastasia Meshcheryakova, PhD (Co-Principal Investigator), and Danube Private University, with Amirreza Mahbod, Ass. Prof. MSc MSc PhD (Co-Principal Investigator). The position offers the opportunity to work in a multidisciplinary team that includes experts from biology, medicine, and computer science in one of the most trending fields of medical image analysis.

The successful postdoctoral candidate will conduct the research at MIAAI at Danube Private University under the main supervision of Amirreza Mahbod, Ass. Prof. MSc MSc PhD in close collaboration with the research group “Molecular Systems Biology and Pathophysiology” led by Diana Mechtcheriakova, Assoc.-Prof. Priv. Doz. Dr. Dipl.-Ing.


Job Duties:

• Developing robust, accurate, and novel deep learning-based algorithms for segmentation and classification at nuclei-based, patch-based, and whole slide image levels in histological images.

• Where applicable, producing interpretable deep learning-based algorithms.

• Creating and controlling annotation frameworks for various objects and structures, defined in the LymphoidStructureMiner project.

• Working with both publicly available and in-house generated image datasets to train and evaluate models.

• Publishing and communicating scientific data in journals and at conferences.


Minimum Qualifications:

• PhD degree in computer science, biomedical engineering, bioinformatics, or a related field

• Strong publication record in subject-specific journals and peer-reviewed conferences

• Experience with deep learning and convolutional neural networks

• Experience with computer vision tasks (e.g., instance segmentation, semantic segmentation, classification, detection, etc.)

• Experience with Python and one of the deep learning frameworks (Tensorflow or PyTorch)

• Experience working with both Windows-based and Linux-based systems.

• Excellent communication skills in English (both oral and written)

• Motivation and ability to work independently


Preferred Qualifications:

• Basic understanding of biology and expertise in medical image analysis, especially in histopathological image analysis.

• Experience in working with advanced Deep Learning-based models, such as vision transformers, generative models, and graph neural networks.



Please provide

• Cover letter and motivation for application

• Curriculum vitae including a list of publications

• Two or more referees – names and addresses of two or more individuals who are willing to provide a letter of recommendation for you


Submit your application documents to amirreza.mahbod(at)

Deadline: April 30, 2024

Interview for the position will be held remotely and/or in person. The exact date will be communicated as soon as the short list has been finalized.


If you have questions, feel free to contact amirreza.mahbod(at)

Project Link:

Nach oben scrollen