Research position at the Center for AI in Radiation Oncology – Medical Data Semantics

Support the design and implementation of comprehensive data models and structured information for oncology Define and standardize clinical, physical, biological, and technical data using Common Data Elements (CDEs) Develop and apply automated systems for extracting, managing, and analyzing unstructured medical data using Natural Language Processing (NLP) Harness Large Language Models (LLMs) and agentic AI frameworks for automated data extraction Conduct retrospective analyses of clinical data to support ongoing AI and clinical projects within CAIRO, leveraging experience with large clinical databases (e.g., NIS, NSQIP, SEER) Act as a bridge between clinical needs and technical execution, translating complex medical workflows into robust data structures Be a part of an active research team across disciplines in a leading university hospital

With the Inselspital, Bern University Hospital, the hospitals Riggisberg, Aarberg, Belp and the Bern Rehabilitation Center Heiligenschwendi, the Insel Gruppe forms the largest full-service medical care system in Switzerland, from cutting-edge medicine to primary care. More than 11,000 employees from 102 nations ensure that our patients are treated according to the latest developments, methods and possibilities in medicine.

• MD, M.SC, or PhD degree in Health Informatics, Computer Science, Data Science, Medicine, Computational Biology, Biomedical Engineering, or a closely related field

• Demonstrated dual expertise, possessing both a strong technical background in data engineering/data science and medical/clinical know-how

• Proven track record and hands-on experience in data semantics, data definition, and health data structuring

• Strong background in machine learning and deep learning, particularly with NLP and LLMs applied to medical or biological data

• A demonstrable portfolio of software, open-source projects, or AI platforms (e.g., GitHub) is highly desired

• Fluent in English (speaking and writing)

• Experience in clinical oncology workflows or comprehensive cancer centers is highly advantageous