The Center for Radiological Research at Columbia University Irving Medical Center seeks a highly motivated postdoctoral researcher to develop and apply cutting-edge causal machine learning methods for precision radiotherapy optimization. This position is funded through the prestigious Empire AI Fellows Program, New York State's initiative to advance computational research using the Empire AI computing infrastructure.
Empire AI is a unique consortium of public and private research institutions advancing AI research for the public good. The candidate will work at the intersection of causal inference, machine learning, and clinical oncology, developing methods to estimate treatment effects from multimodal observational medical data and translating these methods into clinical decision support tools. Research Focus The postdoc will
lead projects in: Causal Machine Learning: Implementing and extending state-of-the-art methods including Double Machine Learning (DML), Targeted Maximum Likelihood Estimation (TMLE), causal forests, and causal foundation models for treatment effect estimation Multimodal Data Integration: Combining tabular, imaging, and text data for precision medicine applications Clinical Translation: Collaborating with radiation
oncologists, radiation biologists, causal inference experts, data scientists and medical physicists to deploy causal ML models in clinical workflows Application Domain: Radiotherapy optimization for cancer treatment, with focus on head and neck cancer, lung cancer, pancreatic cancer and other solid tumors Key Responsibilities Design and implement causal machine learning algorithms for treatment effect estimation at
population and subgroup/individual patient levels Analyze large-scale clinical datasets (electronic health records, cancer registries, clinical trial data) Integrate mechanistic radiobiological models with data-driven causal ML approaches Develop clinical decision support tools in collaboration with physicians and AI engin
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