Postdoctoral Researcher in Multimodal 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. Development of model workflows that can use external tools and knowledge sources in a reliable and auditable way. Development of post-training methods that improve clinical reasoning quality, reliability, and safety. Evaluation of oncology reasoning models in clinically meaningful 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.

• 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

• 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 settings