2 Jobs

Digital prevention, diagnostics and therapy guidance A 20-member team from the fields of medicine, molecular biology and informatics/data science is at the forefront of machine learning for cancer prevention and diagnosis. We are currently working on bringing an AI-powered medical device to market that assists dermatologists in diagnosing melanoma - one of the most dangerous forms of skin cancer while also innovating novel approaches of personalized skin cancer prevention. Help shape the future of personalized skin cancer prevention and diagnostics by developing AI-driven methods that integrate diverse biomedical datasets. We seek real world impact, meaning the PhD project is in collaboration with two large German companies (Beiersdorf and HEINE Optotechnik), to help patients benefit from the developed solutions as fast as possible. As part of this PhD position, you will not only contribute to cutting-edge AI research in dermatology but also lead a key bioinformatics project aimed at integrating multiple data sources for personalized skin cancer prevention. Your work will be instrumental in building a scalable bioinformatic infrastructure that connects epigenetic data, microbiome analyses, and AI-enhanced dermatoscopic imaging. Development of AI-driven models for skin cancer diagnostics and early detection, with a focus on robustness, explainability, and clinical applicability. Building a bioinformatic pipeline that integrates epigenetic profiling (TapeLift), microbiome data, and AI-based image analysis to create predictive models for personalized prevention strategies. Designing and validating a digital dashboard for real-time risk stratification and clinical decision-making, ensuring accessibility for both clinicians and patients. Collaborating with interdisciplinary experts , including dermatologists, AI researchers, statisticians, and computational biologists, to optimize data-driven prevention strategies. Validating the developed models and infrastructure through pilot studies with data from 100 study participants, ensuring real-world applicability. Publishing in top-tier scientific journals and contributing to clinical studies to validate the effectiveness of AI-assisted diagnostics.