Postdoctoral Researcher / Postdoctoral Researcher

Postdoctoral Researcher in Multimodal Reasoning Models for Oncology

100%, Basel, fixed-term

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 integrate language, vision, biomedical knowledge, clinical context, and relevant patient-level data to produce reliable, auditable, and uncertainty-aware outputs.

A significant aspect of this 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 capable of justifying recommendations, citing supporting evidence, calibrating uncertainty, and deferring appropriately while maintaining safety in clinically realistic settings.

This position is part of a highly interdisciplinary collaboration between ETH Zurich, Kaiko.ai, and clinical partners, providing an opportunity to advance foundational AI research while working toward real-world translation in oncology.

Job Description

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.

• 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 utilize external tools and knowledge sources in a reliable and auditable manner.

• 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 enhance 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

Clinical Evaluation and Safety

Evaluation of oncology reasoning models in clinically meaningful settings.

• 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

Profile

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 settings

Workplace

We offer a full-time postdoctoral position at ETH Zurich, one of the world's leading research universities. This project is a collaboration between our lab at ETH Zurich (D-BSSE located in Basel) and Kaiko.ai. You will have the opportunity to work on cutting-edge foundation models for real-world oncology reasoning, with access to unique multimodal clinical datasets and close collaboration with Kaiko.ai and clinical partners. Our highly interdisciplinary environment spans AI (foundation models, MLLMs, agent systems), oncology, and clinical informatics. We provide a competitive salary and excellent research infrastructure, including access to the Alps cluster with 10k high-end GPUs within SwissAI projects. Our group is actively engaged with the ETH AI Center and SwissAI initiative, giving our members access to a vibrant and world-class AI community.

We Value Diversity and Sustainability

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.

Curious? So Are We.

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, a list of names that can be queried for letters of recommendation will suffice)

For further information about our research group, please visit our website. Questions regarding the position should be directed to mmoorlab@gmail.com. Please note that only applications matching the job profile will be considered. Apply online using the form below.

About ETH Zurich

ETH Zurich is one of the world's leading universities specializing in science and technology. We are renowned for our excellent education, cutting-edge fundamental research, and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.