Michael Moor
3 months ago
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Postdoctoral Researcher in Multimodal Reasoning Models for Oncology at ETH Zurich ETH Zurich in Switzerland
Degree Level
Postdoc
Field of study
Oncology
Funding
Full-time postdoctoral position at ETH Zurich with competitive salary and excellent research infrastructure. The project offers access to the Alps cluster with 10k high-end GPUs within SwissAI projects and collaboration with Kaiko.ai and clinical partners.
Deadline
Expired
Country
Switzerland
University
ETH Zurich
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About this position
ETH Zurich is advertising a Postdoctoral Researcher position in Multimodal Reasoning Models for Oncology within the Medical AI Lab led by Michael Moor at the D-BSSE in Basel, in collaboration with Kaiko.ai and clinical partners.
The project focuses on developing, post-training, and evaluating flexible AI systems for oncology that combine language models, vision, biomedical knowledge, clinical context, and patient-level multimodal data. Research topics include foundation models, multimodal language model architectures, retrieval from literature and clinical guidelines, tool-augmented inference, multi-agent workflows, process supervision, verifier-guided training, and reinforcement learning-based post-training.
The role is designed for a highly motivated researcher who wants to work on clinically grounded AI for diagnosis, molecular interpretation, treatment selection, longitudinal care, uncertainty calibration, abstention, and safety-aware reasoning. The position emphasizes traceable, auditable outputs and evaluation in clinically realistic settings, including guideline concordance, diagnostic and therapeutic reasoning quality, tool-use reliability, citation quality, and clinician-in-the-loop assessment.
Eligibility: applicants must hold a PhD in Computer Science, Machine Learning, Medical AI, Biomedical Informatics, Computational Biology, or a related field. Strong Python and modern ML framework skills are required, along with experience in deep learning and large language models. A strong publication record in AI/ML or related biomedical fields is expected. Preferred experience includes multimodal models, biomedical/clinical language models, reasoning agents, LLM post-training, retrieval methods, and scalable GPU training.
Funding and environment: this is a full-time postdoctoral position at ETH Zurich with competitive salary and excellent research infrastructure. The group has access to large-scale GPU resources through SwissAI projects, including the Alps cluster, and offers a highly interdisciplinary environment spanning AI, oncology, and clinical informatics.
Application: submit your application only through the ETH Zurich online portal. The application package should be combined into one PDF and include a CV with significant publications, Bachelor and Master transcripts, a motivation letter, and letters of recommendation if available. The group plans to collect applications for about one month, with the stated deadline of 19 July.
Funding details
Full-time postdoctoral position at ETH Zurich with competitive salary and excellent research infrastructure. The project offers access to the Alps cluster with 10k high-end GPUs within SwissAI projects and collaboration with Kaiko.ai and clinical partners.
What's required
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 includes foundation models, multimodal models, biomedical/clinical language models, reasoning models, agents, tool use, compound LLM systems, LLM post-training methods such as RLHF, RLAIF, verifier-guided training, or process supervision, retrieval methods, medical AI applications (especially oncology, genomics, imaging, or clinical NLP), and scalable ML infrastructure or multi-node GPU training.
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