AI is no longer only changing software. It is now changing how scientists study cells, diseases, images, and complex biomedical data.
That is why this paid PhD position at the Medical University of Vienna is a strong opportunity for students with a background in computer science, bioinformatics, applied mathematics, physics, machine learning, or a closely related field.
The position is based at the University Clinic for Internal Medicine III / Clinical Division of Gastroenterology and Hepatology and is expected to start from 1 September 2026. It is a full-time PhD position with a workload of 40 hours per week.
| Feature | Details |
|---|---|
| Host Institution | Medical University of Vienna |
| Country | Austria |
| Position Type | Paid PhD Position |
| Field | Machine Learning for Biomedicine |
| Job Code | 272/26 |
| Department | Internal Medicine III / Gastroenterology and Hepatology |
| Start Date | Expected from 1 September 2026 |
| Workload | Full-time, 40 hours per week |
| Duration | 4 years, with option for extension |
| Salary | €3,776 gross monthly, paid 14 times per year |
| Application Deadline | 3 June 2026 |
| Application Method | Email application |
| Application Email | bewerbungen@meduniwien.ac.at |
About the Medical University of Vienna PhD Position
The Medical University of Vienna is recruiting for a PhD position focused on machine learning for multimodal biomedical data. The successful candidate will work on new machine learning models, especially generative and mechanistic approaches, for large biomedical datasets.
This is not a regular classroom-based PhD. It is a research employment position where the candidate will work on a dissertation while also contributing to active research projects, teaching, student supervision, scientific publications, and conference presentations.
The position is especially attractive for applicants who want to work at the point where AI, biology, medicine, statistics, and computational modelling meet.
What the Research Will Focus On
The selected PhD candidate will develop machine learning models for biomedical data. The work will involve large-scale datasets such as single-cell omics, spatial omics, and biomedical imaging data.
The goal is not just to build models that work. The research also aims to build models that are scalable, reproducible, and biologically meaningful.
The candidate will also help design, implement, and evaluate computational pipelines. This means the work may involve coding, data processing, model development, testing, collaboration with clinical and experimental partners, and turning research results into strong academic outputs.
If your interest is in research that connects data, technology, and real-world societal problems, you may also want to read about this fully funded UCD PhD studentship in Ireland on AI and climate misinformation. It is a different field, but it also sits at the intersection of AI, research, and human impact.
Salary and Funding Details
This is a paid PhD position. The official posting states that the minimum monthly salary is currently €3,776 gross, paid 14 times per year. The amount may increase depending on relevant previous experience and collective agreement rules.
The employment relationship is limited to 4 years, with the possibility of extension.
For students comparing paid research roles in Europe, this structure is similar to other salary-based research opportunities. For example, the University of Bonn 32 paid PhD and postdoc positions in Germany also follows a paid researcher model rather than a simple scholarship stipend.
Who Can Apply?
Applicants should have a completed degree or master’s degree in Computer Science, Bioinformatics, Applied Mathematics, Physics, or a closely related field.
The official requirements also include participation in the PhD programme, willingness to contribute to research and teaching, strong Python programming skills, and experience with machine learning frameworks such as PyTorch or JAX.
Applicants should also have solid knowledge of machine learning, statistics, and related fields. The university is looking for someone who can work independently, think analytically, and contribute to interdisciplinary research at the interface of AI and biology.
Skills That Will Strengthen Your Application
The position will suit applicants who already have experience with machine learning and biomedical or biological data.
Experience with any of the following will be useful:
Generative models
Probabilistic modelling
Graph-based methods
Single-cell or spatial omics data
Biomedical imaging
Large datasets
GPU-based computing
These skills are listed in the official job posting as relevant experience for the role.
Required Application Documents
Applicants must send a complete application. The official posting mentions the following documents:
CV
Cover letter
Relevant certificates and documents proving the required qualifications
When applying, applicants must include the job code 272/26 in the subject line. Applications should be sent by email to:bewerbungen@meduniwien.ac.at
The application deadline is 3 June 2026.
How to Apply
To apply, prepare your CV, cover letter, and relevant certificates. Then send your full application by email before the deadline.
Application Email: bewerbungen@meduniwien.ac.at
Subject Line: Kennzahl 272/26
Final Advice for Applicants
This PhD position is best for applicants who can show both technical strength and research maturity. Your cover letter should not only say that you know machine learning. It should show how your background fits biomedical data, reproducible modelling, and interdisciplinary research.
If you have worked with Python, PyTorch, JAX, omics data, biomedical images, large datasets, or GPU-based modelling, make that clear in your application.
And if you are also exploring other PhD funding options in the UK, especially support-based opportunities for at-risk scholars, you can check the Loughborough University Sanctuary PhD Scholarship.
For applicants who want to build a research career in AI for biomedicine, this Medical University of Vienna paid PhD position is a strong opportunity to work on meaningful machine learning research in a leading medical university environment.







