Postdoctoral Researcher in Multimodal Reasoning Models for Oncology

Immigration Policy Lab; Basel; Vor Ort; CHF 80.000 - 100.000; Vollzeit

Benefits dieser Stelle

Competitive salary

Access to unique multimodal clinical datasets

Excellent research infrastructure

Interdisciplinary environment

Zusammenfassung

The Immigration Policy Lab in Basel is seeking a Postdoctoral Researcher to lead research on multimodal reasoning models for oncology. This role focuses on developing AI models that combine language, vision, and clinical data for diagnostic and therapeutic decision-making.

Candidates should have a PhD in relevant fields, strong programming skills, and experience with deep learning. This position offers collaboration with ETH Zurich and Kaiko.ai, competitive salary, and access to unique multimodal clinical datasets.

Qualifikationen

• 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.

Aufgaben

• Develop multimodal language model architectures.
• Integrate clinical context and patient-level multimodal evidence.
• Adapt and evaluate on public and institutional oncology datasets.
• Develop uncertainty-aware and safety-aware reasoning behavior.

Kenntnisse

Programming skills in Python

Experience with deep learning

Strong publication record in AI/ML

Ability to work in interdisciplinary environments

Ausbildung

PhD in Computer Science or related field

Tools

Modern ML frameworks

Jobbeschreibung

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.

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.

Key responsibilities

• 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

• 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

• 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

• 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

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

We value diversity and sustainability

ETH Zurich encourages an inclusive culture, promotes equality of opportunity, values diversity, and nurtures a working and learning environment in which the rights and dignity of all staff and students are respected. Sustainability is a core value for us – we are consistently working towards a climate‑neutral future.