PhD Position: Machine Learning / Medical Imaging

5 Nov 2025
Job Information

Organisation/Company

Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy

Research Field

Computer science » Modelling tools
Computer science » Programming
Computer science » Other
Medical sciences » Other

Researcher Profile

First Stage Researcher (R1)

Positions

PhD Positions

Country

Austria

Application Deadline

30 Nov 2025 - 23:55 (Europe/Vienna)

Type of Contract

Temporary

Job Status

Part-time

Offer Starting Date

3 Nov 2025

Is the job funded through the EU Research Framework Programme?

Other EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

AutoPiX connects clinical expertise, multimodal imaging and cutting-edge AI to address urgent unmet needs and provide more precise an personalized care in arthritis – a group of chronic diseases that cause inflammation, joint destruction and disability. The project unites one of the world’s largest curated arthritis imaging collections (>100,000 X-ray, ultrasound and MRI scans across sites and longitudinal timepoints) with rich clinical and laboratory metadata, and an international IHI-funded consortium of academia, pharma, medtech, startups and patient partners.

 

Your Mission as PhD is to develop novel machine-learning methods to (1) detect small, clinically meaningful changes in joints, (2) enable robust longitudinal monitoring and treatment-response prediction, and (3) build a multimodal Visual Foundation Model for arthritis that boosts downstream tasks and enables discovery of new biomarkers and arthritis subtypes. Ultimately, this should lead to earlier diagnosis, improved monitoring and better-tailored treatments. Technical directions include self-supervised and multimodal representation learning, explainability and domain-robustness across sites and devices. 

 

This role is ideal for a candidate who is passioned about self-driven high-impact ML research with immediate clinical translation, access to large, heterogeneous real-world data, strong compute resources, industry collaborations and mentorship aimed at publishing high-impact ML/medical-imaging papers and clinical impact.

What you’ll work on

• Develop and benchmark novel machine learning models for detecting subtle, clinically relevant changes in arthritis imaging.
• Create robust algorithms for longitudinal disease monitoring and treatment-response prediction.
• Build a multimodal Visual Foundation Model for arthritis that integrates imaging, clinical and laboratory data.
• Advance methods for explainability, domain robustness and federated or privacy-preserving analysis.
• Collaborate with clinicians, imaging experts, data scientists and industry partners to ensure clinical translation.
• Present and publish your research at top-tier ML and medical imaging conferences.

What you’ll learn (hands on skills)

• Advanced ML research skills: multimodal representation learning, foundation model design, explainability and domain adaptation.
• Handling and analyzing large, multi-site medical imaging datasets across modalities (X-ray, ultrasound, MRI).
• Scalable ML workflows: GPU-based training, experiment tracking, reproducible pipelines, model validation and deployment.
• Research excellence: hypothesis formulation, experimental design, statistical evaluation, scientific writing and presentation.
• Translating clinical problems into AI tasks and working effectively across disciplines with clinicians, industry and patient partners.

Your research environment

You will be embedded in the Computational Imaging Research Lab (CIR), affiliated with the Comprehensive Center for AI in Medicine (CAIM) and the Division of Rheumatology at the Medical University of Vienna, and will be an integral part of the international AutoPiX consortium (academia, pharma, medtech, startups, patient partners).

 

 

Where to apply

Website

https://oc10.meduniwien.ac.at/open-phd-positions

Requirements

Research Field

Computer science

Education Level

Master Degree or equivalent

Skills/Qualifications

Requirements:

• Master degree (MSc or equivalent) in Computer Science, Machine Learning or a related discipline.
• Strong foundation in machine learning / deep learning (theory + hands-on). Evidence may include a thesis, open-source project, or publications.
• Programming/engineering expertise. Proficient in Python and practical with a deep-learning framework (PyTorch or TensorFlow). Comfortable writing clean, reproducible code and using version control.
• Analytical mindset & problem-solving drive.
• You are passionate about formulating hard research questions, designing experiments, and iterating to robust solutions.
• You enjoy interdisciplinary collaboration, mentoring and sharing knowledge with peers and students. 

Advantage: 

• Prior experience with medical imaging or multi-site clinical datasets.
• Experience with multimodal or self-supervised learning, longitudinal modelling, foundation-models, or explainable AI.
• Familiarity with training on GPU clusters, experiment tracking, containerization, and reproducible pipelines.
• Evidence of research potential for top venues (e.g., first-author papers, strong project results).
• German is a bonus

Languages

ENGLISH

Level

Excellent

Additional Information
Benefits

• Close collaboration with other AutoPiX PhD students, regular joint training, workshops, and research retreats.
• Great Work Environment: Friendly, international team in one of the world’s most livable cities, with health programs, an on-site cafeteria and excellent public transport.
• Top-Tier Mentorship: leading experts/supervisors in AI, rheumatology and clinical imaging, for high-impact publishing.
• Exceptional resources: access to one of the largest curated arthritis imaging datasets and high-performance GPU clusters.
• Career & real-world impact: access to conferences, strong pathways into academia, industry R&D or clinical-translational roles, plus networking with patient advocates to ensure clinical relevance.

Eligibility criteria

• Applicants must hold a Master’s degree from an accredited university.
  • You may also apply if you expect to complete your Master’s degree within the next six months.
• Relevant research experience is highly advantageous and will strengthen your application in the competitive selection process.
• Internship experience is considered as a plus.
• Candidates should demonstrate a genuine interest in the research area, a commitment to scientific inquiry, and the ability to address complex research challenges.
• English language proficiency at a minimum C1 level (according to the CEFR) is required for effective communication within our international research community. Proof of English proficiency must be provided upon admission.

Selection process

Document Check:

• The recruitment board reviews all submitted applications.
• Candidates are shortlisted for the next stage based on eligibility and qualifications.

Online Interviews:

• Scheduled for mid December 2025
• Shortlisted candidates will meet with up to three principal investigators (PIs) to discuss their research interests and preferred projects.
• Further shortlisting occurs after these interviews to determine candidates for the next step.

Campus Visit:

• Final in-person interviews will take place in Vienna end of January 2026
• Candidates must be available on these fixed dates.
• Additional shortlisting will occur after the campus visit.

Offer and Waiting List:

• Following the campus visit, selected candidates will receive offers to join the program.
• A waiting list will also be created for other strong candidates who may be offered a position later.

Additional comments

Application Process

All applications must be submitted via the online portal no later than 30 November 2025: https://oc10.meduniwien.ac.at/open-phd-positions

Please note that applications sent by email cannot be considered. There is no need to contact the principal investigator (PI) prior to submitting your application.

Submission is possible only after two reference letters have been received in the system.

Further details, including the FAQ and Application Guide, are available at:
https://www.meduniwien.ac.at/web/en/studierende/mein-studium/phd-programme-un094/forms/our-phd-programs/

Website for additional job details: https://oc10.meduniwien.ac.at/open-phd-positions

Website for additional job details

https://oc10.meduniwien.ac.at/open-phd-positions

Work Location(s)

Number of offers available

1

Company/Institute

Medical University of Vienna

Country

Austria

City

Vienna

Postal Code

1090

Geofield

Contact

State/Province

Vienna

City

Vienna

Website

https://oc10.meduniwien.ac.at/open-phd-positions
https://www.cir.meduniwien.ac.at/

Street

Spitalgasse 23

Postal Code

1090

E-Mail

phdrecruitment@meduniwien.ac.at

STATUS: EXPIRED