Explainable Medical Artificial Intelligence (XAI in healthcare) is the focus of an organized, multidisciplinary research training program at Ulm University in Germany that offers fully financed PhD seats. The goal of this program is to teach PhD researchers how to create AI systems that are not only strong but also transparent, understandable, moral, and clinically dependable, guaranteeing their safe application in actual medical settings.
Advanced healthcare applications such as clinical decision support systems, predictive medicine, medical diagnostics, and patient monitoring technologies are the main focus of this research. In order to increase trust, accountability, and adoption in healthcare practice, a major focus is on making AI models intelligible for medical professionals.
The DFG-funded Research Training Group (KEMAI), an organized and cooperative program that unites many fields, includes these PhD places. The issues of developing next-generation medical AI systems that are both technically sophisticated and socially responsible are closely addressed by researchers from computer science, medicine, data science, and ethics.
A well-run doctorate training environment, interdisciplinary supervision, access to state-of-the-art research facilities, and chances to work with professionals in both academic and hospital institutions are all advantages for candidates chosen for the program. Additionally, the curriculum places a strong emphasis on professional growth, scientific collaboration, and contributions to high-impact medical artificial intelligence research.
Host Country: Germany
Institution: Ulm University
Opportunity Type: PhD (Doctoral Research)
Field of Study: Medical AI, Machine Learning, Healthcare AI, Bioinformatics
Eligible Applicants: International Students
Financial Coverage: Fully Funded
Research Focus
The KEMAI programme is centered on advancing research in the following areas:
- Explainable artificial intelligence for medical decision-making processes.
- Development of transparent and interpretable machine learning models.
- Application of AI in clinical settings, including diagnostics and treatment planning.
- Investigation of ethical, legal, and societal implications of medical AI systems.
- Integration of artificial intelligence solutions into real-world healthcare workflows.
- Promotion of interdisciplinary collaboration between medicine, computer science, and related fields.
Eligibility Criteria:
- own a master’s degree in a field of study that is pertinent.
- Show that you have a solid academic background and research experience.
- Demonstrate a genuine interest in trustworthy, ethical, and explainable AI systems.
- possess prior expertise in data analysis, machine learning, or similar technical fields (recommended).
- Be able and eager to collaborate on multidisciplinary research projects.
- Provide a compelling case for medical AI research.
Preferred Requirements
- prior experience conducting research in AI, medical data, or similar fields.
- strong programming abilities and familiarity with machine learning methods.
- publications, theses, or scholarly endeavors in related fields.
- knowledge of the clinical difficulties and ethical issues in AI applications.
Benefits:
- PhD job contract with full funding under the German public sector salary scale E13.
- The monthly research salary is calculated using the public employment pay system in Germany.
- full reimbursement for research-related costs and tuition.
- social security benefits and full health insurance.
- access to cutting-edge medical and AI research facilities.
- training in a robust academic setting that is multidisciplinary.
- chances for research to be published in prestigious international publications.
- involvement in international conferences and joint research initiatives.
- Contract extension is possible for a maximum of one more year.
Required Documents
- Updated academic CV.
- Master’s degree certificate(s) along with official transcripts.
- Motivation letter or statement of research interest.
- Academic reference or recommendation letters.
- Proof of English language proficiency, if applicable.
- Relevant research documents such as thesis, publications, or academic portfolio.
Deadline: 31 August 2026
