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project on transparent, agent-based AI methods to support multidisciplinary tumor boards in oncology. You will develop methods to transform heterogeneous oncology documentation into structured, time-aligned
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) and handling big data (sequencing, mass spectrometry, or similar) • Enthusiasm and background in establishing techniques in molecular biology, data management (big data, sequencing analyses) and related
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for more than 3,500 treatment courses, aligned with the OMOP Common Data Model and robust data-quality rules Develop and evaluate agent-based AI functions for therapy-sequence support, clinical-trial matching and
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solutions to PDEs . Your work will involve utilizing GPU code generators for matrix multiplication kernels and ensuring the software is usable in Python for downstream data analysis [2]. Collaboration is
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to align requirements and validation scenarios Preparing research findings for internal project meetings, scientific publications, and conference contributions Contributing to open-source software, technical
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interdisciplinary research unit involving multiple institutions and 14 doctoral researchers Flexible working hours and the possibility of working partially from home We offer a 75% position classified according
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. • Expertise in Flow Cytometry, particularly in multi-color FACS staining and cell population analysis. • Experience/Interest in single cell RNA sequencing experiments. • Excellent proficiency in English, both