• Published On: October 6th, 2025Categories: News

    CRIION-Awarded Argentine Team Develops AI Tool to Predict the Most Effective Cancer Therapy for Each Patient

    We would like to draw your attention to a recent article published by TN on October 3, 2025, highlighting a groundbreaking project by Argentine scientists who have developed an AI-driven tool to predict the most effective cancer treatments for individual patients. The innovation represents a promising step forward in the field of personalized oncology. The research(...)

  • Published On: September 30th, 2025Categories: News

    CRIION Professorship for Bioinformatics: AI for Oncology Research

    Mertelsmann Foundation provides €1.3 million funding over six years. Jun.-Prof. Dr. Maria Kalweit begins her Tenure Track CRIION Professorship for Bioinformatics: AI for Oncology Research at the University of Freiburg. As the head of the newly established chair, she will build a team dedicated to developing trustworthy, robust, efficient, and explainable AI systems that support(...)

  • Published On: September 30th, 2025Categories: News

    Artificial Intelligence and the 2,500-Year-Old Project Called Europe

    The Mertelsmann Foundation, in cooperation with the University of Freiburg, is pleased to invite you to the following event hosted by FRIAS at the University of Freiburg. When? 28 October 2025, 17:00 Where? Anatomie Hörsaal Albertstraße 17, 79104 Freiburg For Whom? Open to the Public Contact max.bolze@frias.uni-freiburg.de Phone: +49 761 203 97407 Further information about(...)

  • Published On: September 30th, 2025Categories: News

    CRIION-affiliated PhD Student Mehdi Naouar is presenting at MICCAI 2025 in Daejeon!

    Mehdi Naouar is presenting his work at the 28th Medical Image Computing and Computer Assisted Intervention. One For All: A Unified Approach to Classification and Self-Explanation His explainability approach One For All is a unified framework that jointly optimizes classification and explanation without the overhead of separate explainability models. This is particularly crucial in health(...)