Postdoctoral Researcher in Multimodal Reasoning Models for Oncology — ETH Zürich
NewCHF 62'000 - 94'000
ETH Zürich · Basel (BS)
- Location
- Basel
- Contract
- fixed-term
- Posted
- 4 days ago
SalaryCHF 62'000 - 94'000
Role overview
Postdoctoral Researcher in Multimodal Reasoning Models for Oncology 100%, Basel, fixed-term print Drucken 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.
- Postdoctoral Researcher in Multimodal Reasoning Models for Oncology 100%, Basel, fixed-term print Drucken 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.
Additional details
- 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
- 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.
- 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.
- 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
- Experience with foundation models, multimodal models, or biomedical/clinical language models
Notes and original content
- 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.
- This may include:
- Multimodal language model architectures
- Examples include:
- 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
