Postdoctoral Researcher in Multimodal Reasoning Models Oncology

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.

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.

Major focus: 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 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.

Job description

Development and adaptation of oncology‑focused foundation models capable of reasoning over complex clinical questions, including diagnosis, molecular interpretation, treatment selection, and longitudinal care.

• 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

Development of model workflows that can use external tools and knowledge sources in a reliable and auditable way.

• 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

Development of post‑training methods that improve clinical reasoning quality, reliability, and safety.

• 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

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

Must Have

• PhD in Computer Science, Machine Learning, Medical AI, Biomedical Informatics, Computational Biology, or a related field
• Strong programming skills in Python and modern ML frameworks
• Experience with deep learning and large language models
• Strong publication record in AI/ML, medical AI, computational biology, biomedical informatics, or related areas
• Ability to work in highly interdisciplinary research environments

Preferred

• Experience with foundation models, multimodal models, or biomedical/clinical language models
• Experience with reasoning models, agents, tool use, or compound LLM systems
• Experience with LLM post‑training methods such as RLHF, RLAIF, verifier‑guided training, or process supervision
• Familiarity with retrieval methods for LLMs, including dense/sparse retrieval, agentic retrieval, or hybrid approaches
• Experience with medical AI applications, particularly oncology, genomics, imaging, or clinical NLP is a plus, but not required
• Experience with scalable ML infrastructure, multi‑node GPU training, or local/private deployment…