We are seeking an exceptional and highly motivated Postdoctoral Researcher to lead research on multimodal reasoning models for oncology. The project focuses on developing, post-training, and evaluating flexible AI models that can support complex oncologic diagnostic and therapeutic decision-making in a safe, transparent, and clinically grounded manner.
The successful candidate will work on oncology-focused multimodal reasoning models that combine language, vision, biomedical knowledge, clinical context, and relevant patient-level data to produce reliable, auditable, and uncertainty-aware outputs.
A major focus of the position is the development of AI-based reasoning strategies for oncology, including tool-augmented inference, multi-agent or compound model workflows, process supervision, verifier-guided training, and reinforcement learning-based post-training. The goal is to build systems that can justify recommendations, cite supporting evidence, calibrate uncertainty, defer appropriately, and operate safely in clinically realistic settings.
This position is embedded within a highly interdisciplinary collaboration between ETH Zurich, Kaiko.ai, and clinical partners, offering an opportunity to advance foundational AI research while working toward real-world translation in oncology.
Reasoning Models for Oncology
Development and adaptation of oncology-focused foundation models capable of reasoning over complex clinical questions, including diagnosis, molecular interpretation, treatment selection, and longitudinal care.
This may include:
- Multimodal language model architectures
- Integration of clinical context, biomedical literature, guidelines, and patient-level multimodal evidence
- Adaptation and evaluation on public and institutional oncology datasets
- Development of uncertainty-aware and safety-aware reasoning behavior
Reasoning Strategies, Agents, and Tool Use
Development of model workflows that can use external tools and knowledge sources in a reliable and auditable way.
Examples include:
- Retrieval from literature, clinical guidelines, and trial databases
- Clinical trial matching and therapy evidence lookup
- Variant interpretation and molecular knowledgebase use
- Multi-agent systems for decomposing complex oncology tasks into hierarchical context streams
- Citation-grounded and traceable outputs suitable for expert review
Process Supervision and Post-Training
Development of post-training methods that improve clinical reasoning quality, reliability, and safety.
This may include:
- Process-level supervision for intermediate reasoning steps
- Outcome-based supervision using expert or guideline-derived signals
- Reinforcement learning for oncology-specific reasoning behavior
- Comparison and development of RL training approaches
- Calibration, abstention, and safety-aware optimization
Clinical Evaluation and Safety
Evaluation of oncology reasoning models in clinically meaningful settings.
Key evaluation dimensions include:
- Guideline concordance
- Diagnostic and therapeutic reasoning quality
- Molecular interpretation accuracy
- Tool-use reliability
- Citation quality and evidence grounding
- Calibration, uncertainty, and appropriate deferral
- Trace auditability and clinician-in-the-loop evaluation
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.
We look forward to receiving your online application with the following documents (concatenated into one PDF):
- CV (including a list of most significant publications)
- Bachelor and Master transcripts
- Motivation letter (motivation & fit to the project and the host lab)
- Letters of recommendation (if available, also just a list of names that can be queried for letters of recommendation will suffice)
Further information about our research group can be found on our Website. Questions regarding the position should be directed to [email protected](no applications).
Please note that we exclusively accept applications submitted through our online application portal. Applications via email, social media, or postal services will not be considered. We plan to collect applications for 1 month (until July 19), and reserve the option to extend this window.
