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of cancer by analyzing and integrating multi-omics and clinical data. Key Responsibilities Analyze and interpret multi-omics datasets, including genomic, transcriptomic, and clinical data from over 7,000
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of information processing), and formal epistemology (in particular, formal representations of inattentiveness and unawareness). This project will involve philosophical / linguistic analysis, experiments with human
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evaluation methods for vision models Design metrics to compare neural and behavioral data with computational predictions Collaborate with CRC groups on open, reproducible standards for model evaluation Mentor
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research, data science, cognitive science, or related disciplines who are eager to engage deeply in the Institute’s research activities in empirical education. We especially welcome applications from
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. Furthermore, we develop advanced scattering methods and machine learning tools for data analysis. For more information, see www.soft-matter.uni-tuebingen.de Qualification and skills Candidates should have good
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safe machine learning and its certification, and implementation of regulation frameworks like the EU AI Act, General Data Protection Regulation (GDPR) and the Medical Devices Regulation (MDR) Very good
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committed to equal opportunities and diversity. It therefore takes individual situations into account and asks for relevant information. The University of Tübingen seeks to raise the number of women in
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journals and edited volumes, and present your work at international conferences. Collaborate with our digital humanities team to engage actively in the development and data curation for the "OMnibus of