Postdoctoral Researcher in Multimodal Reasoning Models for 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.
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:
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.
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.
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. Opportunity to work on cutting-edge foundation models for real-world oncology reasoning. Access to unique multimodal clinical datasets and close collaboration with Kaiko.ai and clinical partners. Highly interdisciplinary environment spanning AI (foundation models, MLLMs, agent systems), oncology and clinical informatics. Competitive salary and excellent research infrastructure (e.g. 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 group members access to this vibrant and world-class AI community.
Equal Opportunities and Diversity 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. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.
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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:
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.
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.
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. Opportunity to work on cutting-edge foundation models for real-world oncology reasoning. Access to unique multimodal clinical datasets and close collaboration with Kaiko.ai and clinical partners. Highly interdisciplinary environment spanning AI (foundation models, MLLMs, agent systems), oncology and clinical informatics. Competitive salary and excellent research infrastructure (e.g. 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 group members access to this vibrant and world-class AI community.
Equal Opportunities and Diversity 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. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.
#J-18808-Ljbffr
Postdoctoral Researcher in Multimodal Reasoning Models for Oncology100%, Basel, fixed-term Arbeitgeber: Eidgenössische Technische Hochschule Zürich
Als Software Research & Development Engineer bei ETH Zürich profitieren Sie von einer inspirierenden und kollaborativen Arbeitsumgebung in einer weltweit anerkannten Forschungseinrichtung. Wir bieten spannende Herausforderungen, vielfältige Projekte und die Möglichkeit, Ihre Fähigkeiten weiterzuentwickeln, während Sie an der Schnittstelle zwischen Forschung, Technik und Betrieb arbeiten. Unsere Kultur fördert Vielfalt und Inklusion, und wir setzen uns aktiv für eine nachhaltige Zukunft ein.
Kontaktdaten:
Eidgenössische Technische Hochschule Zürich Recruiting-Team
