Postdoctoral Position in Automizing Organoid Manufacturing

vor 1 Woche

Heidelberg, Deutschland Universität Heidelberg Vollzeit

**Postdoctoral position in automizing organoid manufacturing (f/m/d)**

The position is funded by the Carl-Zeiss-Stiftung (CZS)

**Background**:

- We aim to develop an autonomous platform for organoid culturing and analysis to address key challenges in organoid-based experiments. This interdisciplinary project will leverage robotic systems for high-throughput culturing, integrating multiple automated subsystems with image-based machine learning models. Our objective is to enable robotic decision-making through machine learning, paving the way for a standardized, scalable, and rapid next-generation system for organoid cultivation and assays.
- We are seeking a talented and highly motivated individual with a strong background in electrical engineering, computer science, automation science, or a related field, and convincing expertise in robotic hardware. Experience with machine learning and large language models is highly desirable. Prior experience in a biological setting, such as cell culture, is considered a plus.
- This position is offered as a joint postdoctoral appointment in the groups of Prof. Pernice at the Kirchhoff Institute for Physics and Prof. Boutros at the German Cancer Research Center (DKFZ) and Heidelberg University. The two research groups will provide support for both the biological aspects and the computational and automation components of the project.
- The POEM program aims to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling. Central to its objectives is the development of methods for culturing tailor-made organoids, assembloids and co-organoids for inter-organ communication towards AI-supported large-scale / high-throughput analyses.

**Your Role**:

- Lead the development of a functional prototype by integrating multiple automated subsystems, including liquid handlers, robotic transfer and imaging systems, into a fully autonomous platform
- Adapt machine learning protocols to enable robotic decision-making for targeted organoid growth
- Benchmark the platform against current standard culturing methods, either independently or in collaboration with consortium partners

**Your Profile**:

- Excellent PhD in electrical engineering, computer science, or a related field
- Proven track record of high-level research, demonstrated through peer-reviewed publications and conference presentations
- Strong background in automation, robotics, or related areas of expertise
- Experience with machine learning and large language models is desirable
- Prior experience in a biological laboratory setting is desired

Please include a motivation letter, curriculum vitae, a list of publications, a list of current research support (if applicable), a summary of teaching experience (if applicable), a research summary, a list with 3 or more referees, including contact information.

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