We investigate how AI agents can combine medical images with clinical information, plan and execute analytical steps, learn from feedback, a
Collaborate with our core development team to bring machine learning into production - Design and train ML models on large-scale clinical da
sits within the ML Engineering Area and integrates tightly with the existing MLOps team rather than as a silo.
Mission Neko is redefining what prevention means, from treating illness when it arrives, to sustaining health before it s ever at risk.
Als AI Solutions Architect gestaltest du die strategische und technologische Weiterentwicklung Künstlicher Intelligenz in den IT-Services de
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Als AI Engineer entwickelst und implementierst du innovative KI-Lösungen auf Basis moderner Large Language Models (LLMs) und begleitest dere
bei uns Du baust unseren AI-Tool-Radar auf und entwickelst ihn kontinuierlich weiter, identifizierst passende Tools je Prozess und erstellst
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We investigate how AI agents can combine medical images with clinical information, plan and execute analytical steps, learn from feedback, assess intermediate results, and respond appropriately to uncertainty or failure. Approaches may include reinforcement learning, multimodal learning, vision-language models, planning, tool-augmented language models, probabilistic modelling, and classical medical image analysis. Candidates may work primarily in one of several application areas: multimodal agents for prostate cancer diagnosis and treatment support; auditable analysis of longitudinal imaging for tumour-response assessment; federated multimodal agents connecting oral imaging with systemic health information; or reinforcement learning for agents that navigate and integrate complex data across medical and other scientific domains.
The individual PhD topic will be developed jointly based on the candidate’s interests, expertise, and the methodological questions arising across these applications. You will: develop agents that combine medical images with clinical text and other information; investigate reinforcement learning for planning, tool use, feedback integration, and iterative improvement; apply agentic methods to segmentation, annotation, quantitative analysis, or scientific data exploration; establish rigorous evaluation methods covering accuracy, robustness, generalisation, uncertainty calibration, failure detection, and appropriate abstention; and collaborate with methodological and clinical researchers, publish your results, and contribute reusable software or benchmarks.
You should have: A master’s degree in computer science, data science, mathematics, physics, engineering, or a related discipline A strong foundation in machine learning and strong Python programming skills Practical experience with a deep-learning framework such as PyTorch Experience in at least one relevant area, such as reinforcement learning, computer vision, language models, multimodal learning, or medical-image analysis An analytical and collaborative working style and good communication skills in English Previous experience with medical data is helpful but not required. We also welcome candidates with strong machine-learning fundamentals who want to develop expertise in agentic AI. What you can expect: Scientific freedom to develop your own methodological research question Close supervision and exchange within a cohort of PhD researchers Access to extensive GPU infrastructure, medical-imaging resources, and established research software Collaboration with international machine-learning and clinical partners Support for conference participation, open-source contributions, and professional development Fully funded PhD submission Please submit: your CV academic transcripts a brief research statement of no more than one page describing a question in agentic AI that interests you your preferred application area, or a ranking of the available areas your master’s thesis or another representative piece of scientific work links to relevant publications, software, or code repositories, if available Instead of a generic motivation letter, please use your research statement to explain which scientific question you would like to investigate, why it matters, and how you might approach it We Offer Excellent framework conditions: state-of-the-art equipment and opportunities for international networking at the highest level Access to international research networks Doctoral salary with the usual social
30 days of vacation per year Flexible working hours Possibility of part-time work Family-friendly working environment Sustainable travel to work: subsidized Germany job ticket Unleash your full potential: targeted training and mentoring through the DKFZ International PhD Program and DKFZ Career Service Our Corporate Health Management Program offers a holistic approach to your well-being Contact: Constantin Ulrich Harsy Telefon: +49 6221 42-3552 The positions are initially limited to 3 years. Application Deadline: December 2, 2026 Applications by e-mail cannot be accepted. Please also note that we cannot return applications submitted by post.
Are you interested? Then become part of the DKFZ and join us in contributing to a life without cancer! Apply now We are convinced that an innovative research and working environment thrives on the diversity of its employees.
Therefore, we welcome applications from talented people, regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical ability, religion and age. People with severe disabilities are given preference if they have the same aptitude. Notice: We are subject to the regulations of the Infection Protection Act (IfSG).
Therefore, all our employees must provide proof of immunity against measles. Share this job! Deutsches Krebsforschungszentrum | Im Neuenheimer Feld 280 | 69120 Heidelberg | www.dkfz.de
The German Cancer Research Center (DKFZ) is one of Europe’s largest cancer research centers. “Research for a life without cancer is the mission of our world-class scientists and all our team members. We investigate how cancer develops, identify cancer risk factors and search for new cancer prevention strategies. We develop new methods with which tumors can be diagnosed more precisely and cancer patients can be treated more successfully.
Every contribution counts – whether in research, administration or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Medical Image Computing (MIC) at the German Cancer Research Center (DKFZ) in Heidelberg, starting in January 2027, is offering PhD Positions in Agentic AI for Medical Image Analysis Reference number: 2026-0230 Heidelberg Full-time Medical Image Computing The Division of Medical Image Computing (MIC) is recruiting PhD candidates to develop AI agents that interpret medical images, use analysis tools, evaluate intermediate results, and adapt their approach based on evidence and feedback.
Candidates will join a collaborative research environment with clinical partners, extensive computing infrastructure, and connections to initiatives such as Helmholtz Imaging.
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