Ph.D. Position in AI for Precision Oncology

The Krauthammer lab (at the University of Zurich) and the Wicki Lab (at the University Hospital of Zurich) are seeking a motivated PhD candidate to work on AI-driven research at the intersection of precision oncology and data science, with a particular focus on leveraging multi-modal clinical data to support personalized cancer treatment decisions.

• Developing predictive models to forecast treatment outcomes and side effects based on retrospective observational clinical data.
• Implementing counterfactual inference and causal machine learning methods to identify optimal treatments and evaluate treatment biases.
• Incorporating explainable AI tools (e.g., SHAP values) and uncertainty quantification techniques (e.g., conformal prediction) to enhance the interpretability and reliability of predictions.
• Collaborating closely with oncologists and data scientists to align AI tools with clinical workflows and real-world needs.

• Master's degree (MSc) in computer science, machine learning, statistics, applied mathematics, or a related discipline.
• Proficiency in Python and core scientific computing libraries (e.g., NumPy, SciPy, Scikit-learn, pandas).
• Experience with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX).
• Strong foundational knowledge of machine learning and predictive modeling techniques.

• Knowledge of longitudinal data analysis, time-series modeling, or causal inference methods.
• Experience with explainable AI techniques for model interpretability.
• Familiarity with multi-modal data integration (e.g., clinical, genetic, proteomics).
• Proficiency in Linux systems, Docker, and high-performance computing (HPC) environments.

Information on your application

• A cover letter detailing your motivation and fit for this position.
• A detailed CV.
• Academic transcripts.
• Contact details for three references.

Department of Quantitative Biomedicine