PhD Studentship in Causal RL

Job Summary

To advance foundational research in causal reinforcement learning, the fully funded PhD Studentship in Causal RL will engage a researcher in theoretical foundations, algorithm development, and benchmarking of RL methods, primarily based at the University of Cambridge with remote collaboration opportunities.

Key responsibilities

• Formulate policy learning from biased datasets using causal reasoning
• Develop reinforcement learning algorithms that utilize causal structures for improved generalization
• Evaluate new methods in controlled environments against standard RL benchmarks

Required qualifications

• First-class or upper second-class honours degree in Computer Science, Mathematics, Engineering, Statistics, or a related field
• Strong background in reinforcement learning, machine learning, probabilistic modelling, or control theory
• Proficiency in Python and standard ML libraries (e.g., PyTorch, NumPy, SciPy, scikit-learn)
• Clear scientific writing skills for communicating research effectively
• Eligibility to study at the University of Cambridge, including meeting English language requirements