Post-Doctoral Research Visit F/M Causal inference and mechanistic digital twins for individualized adjuvant treatment in resected lung cancer

Highlights

• Add a causal layer to a mechanistic digital twin for resected lung cancer.
• Emulate target trials of adjuvant therapy using large retrospective cohorts.
• Benchmark methods on semi-synthetic data with known counterfactuals.

What they offer

• Subsidized meals
• Partial reimbursement of public transport costs
• 7 weeks annual leave + 10 RTT days + exceptional leave
• Professional equipment including videoconferencing and computer loan
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Ideal candidate

PhD with strong causal inference background who enjoys bridging rigorous methodology and real clinical impact in lung cancer. Curious about mechanistic modeling and collaborative work with clinicians.

Requirements

• Strong background in causal inference and survival analysis
• Proficiency in R and/or Python
• Interest in dynamical/mechanistic models, mixed-effects experience a plus
• Interest in clinical applications and good communication skills in English