Publisher
source

Heidelberg University

Postdoctoral Research in Human-Centered AI Behavior in Medicine at Heidelberg University Heidelberg University in Germany

Degree Level

Postdoc

Field of study

Computer Science

Funding

Full-time postdoctoral employment (100%) on TV-L E13, temporary contract for 2 years, with collectively agreed remuneration, attractive company pension scheme (VBL), 30 days vacation, and additional family-friendly and health-related benefits.

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Country

Germany

University

Heidelberg University

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Keywords

Computer Science
Data Science
Behavioral Science
Psychology
Cognitive Science
Biomedical Engineering
Medicine
Artificial Intelligence
Medical Science
Insurance
Statistics
Large Language Models

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About this position

Heidelberg University in Germany is advertising a postdoctoral research position in Human-Centered AI Behavior in Medicine at the Medical Faculty in Heidelberg.

The project sits at the intersection of artificial intelligence, medicine, psychology, behavioral science, and healthcare. The successful candidate will work with large language models (LLMs) and vision-language models (VLMs) to study AI behavior, develop evaluation methods, and translate findings into real-world medical applications that improve healthcare outcomes and user experience.

Responsibilities include designing large-scale experiments with LLMs/VLMs, building reproducible evaluation pipelines, developing computational methods for behavioral and psychological characterization of AI systems, analyzing multimodal image and real-world datasets, leading independent research projects, publishing in peer-reviewed journals, presenting at conferences, and supporting grant applications, teaching, and supervision.

Applicants should hold a PhD in Artificial Intelligence, Computer Science, Data Science, Computational Cognitive Science, Medical Informatics, or a closely related quantitative field. Strong experience with LLMs/foundation models/multimodal AI, Python, PyTorch or Hugging Face Transformers, quantitative/statistical analysis, and independent research output is expected. Excellent English communication skills and a collaborative, self-driven mindset are also required.

The role is full-time (100%), classified under TV-L E13, and offered as a temporary 2-year contract. Benefits mentioned include pension coverage (VBL), 30 days vacation, flexible hours, family-friendly arrangements, job ticket, and health/sports offers.

Applications must be submitted online via the provided portal. The post does not state a deadline. Contact details for the hiring team are provided, including Dr. Silvia Barbosa at Heidelberg University/UKHD.

Funding details

Full-time postdoctoral employment (100%) on TV-L E13, temporary contract for 2 years, with collectively agreed remuneration, attractive company pension scheme (VBL), 30 days vacation, and additional family-friendly and health-related benefits.

What's required

PhD in Artificial Intelligence, Computer Science, Data Science, Computational Cognitive Science, Medical Informatics, or a comparable quantitative discipline; hands-on experience with LLMs, foundation models, multimodal AI systems, or related machine-learning technologies; strong Python programming skills and experience with PyTorch, Hugging Face Transformers, or equivalent tools; ability to develop scalable, automated, reproducible computational experiments and data pipelines; experience analyzing complex experimental and large-scale datasets with advanced quantitative and statistical methods; track record of independent scientific research with peer-reviewed publications or comparable achievements; excellent written and verbal English; collaborative, analytical, self-driven mindset with enthusiasm for interdisciplinary research. A valid measles immunity certificate is required for employment at UKHD.

How to apply

Apply online through the Heidelberg University/UKHD application portal using the provided job ad link. For questions, contact the hiring team via the listed email/phone contact.

More information can be found here

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