PhD Student for Machine Learning in Oncology | PhD Student for Machine Learning in Oncology

Job Summary

This PhD position offers a unique opportunity to contribute to cutting-edge cancer research by developing robust and reliable machine learning models for therapy decisions and outcomes. The role focuses on applying causal inference methods to real-world health data, specifically for Acute Myeloid Leukemia (AML). Day-to-day tasks will involve developing causal machine learning methods for survival modeling, building trustworthy recommender systems for therapy decisions using electronic health records, and creating uncertainty-aware models. The ideal candidate will possess a strong background in computer science, statistics, or related fields, with excellent knowledge of machine learning and statistics, and proficiency in Python-based deep learning frameworks. This position is part of the prestigious ERC Consolidator Grant "TAIPO - Trustworthy AI in Personalized Oncology" and involves close collaboration with experimental and clinical partners, making it an exciting and impactful role in the fight against cancer.