-
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
-
invasive sensing tools to monitor metabolites, oxygen, carbon dioxide, pH, and other parameters. Ideally, the methods can function in parallel and on a large scale. The research is vital to understand key
-
. 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
-
. 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
-
Heidelberg University and Stanford University, including population health researchers, clinicians, and methodologists. The researcher will lead analyses in large-scale electronic health record data