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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
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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
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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
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. 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
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. 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
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Heidelberg University and Stanford University, including population health researchers, clinicians, and methodologists. The researcher will lead analyses in large-scale electronic health record data
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, troubleshooting and routine upkeep Develop, optimize and validate sample-preparation and data-analysis workflows for spatial metabolomics, lipidomics and multimodal studies Act as contact for internal and external
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infrastructure. The position entails active involvement in both experimental procedures and computational data analysis, alongside the improvement of advanced methodologies, including single-cell proteomics
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regarding the position, please contact Prof. Dr. Renata Motta via hcias-jobs@uni-heidelberg.de . Further information on the HCIAS can be found at www.hcias.uni-heidelberg.de/en . We look forward to receiving
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, extension is sought) Contract:TV-L Your tasks Molecular laboratory work with 2D and 3D cell culture models Experimental design, data analysis and interpretation Publishing research findings in peer-reviewed