PhD Studentship: Causal Reinforcement Learning

About Phaidra Phaidra is building the future of industrial automation. The world today is filled with static, monolithic infrastructure. Factories, power plants, buildings, etc. operate the same they've operated for decades — because the controls programming is hard-coded. Thousands of lines of rules and heuristics that define how the machines interact with each other. The result of all this hard-coding is that facilities are frozen in time, unable to adapt to their environment while their performance slowly degrades. Phaidra creates AI-powered control systems for the industrial sector, enabling industrial facilities to automatically learn and improve over time. Specifically: We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data into high-value actions and decisions. We focus on industrial applications, which tend to be well-sensorized with measurable KPIs — perfect for reinforcement learning. We enable domain experts (our users) to configure the AI control systems (i.e. agents) without writing code. They define what they want their AI agents to do, and we do it for them. Our team has a track record of applying AI to some of the toughest problems. From achieving superhuman performance with DeepMind's AlphaGo , to reducing the energy required to cool Google's Data Centers by 40%, we deeply understand AI and how to apply it in production for massive impact. Phaidra’s ability to achieve its mission is determined by our ability to work together — as defined by our core values: Transparency , Collaboration , Operational Excellence , Ownership , and Empathy. We seek individuals who embody these values, as they are instrumental in ensuring our team consistently delivers excellence and fosters an engaging and supportive culture Phaidra is based in the USA, but we are 100% remote with no physical office. We hire employees internationally with the help of our partner, OysterHR . Our team is currently located throughout the USA, Canada, UK, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India. About the Project Phaidra builds autonomous AI control systems for data centre and industrial infrastructure. We deploy reinforcement learning in production on some of the world's most complex physical systems. The hard, unsolved research problems are the same ones that matter in practice. This studentship is an opportunity to work on foundational RL research while staying grounded in real-world challenges. Reinforcement Learning (RL) has emerged as a powerful framework for sequential decision-making. Yet a fundamental limitation remains: agents trained on historical data under fixed policies often exploit spurious correlations that break at deployment time, especially when the environment shifts or the new policy explores previously unseen regions of the state-action space. This PhD project tackles that limitation by integrating causal reasoning into RL. Causal inference provides a formal language (causal graphs, interventional queries, counterfactuals) for distinguishing stable structural relationships from incidental correlations. The research will investigate how these tools can make RL agents more robust and generalizable, particularly in real-world industrial settings. The project will proceed in three phases: Theoretical Foundations : formalising policy learning from biased, small datasets through a causal lens; characterising how confounding and mediators affect offline RL. Algorithm Development : building RL algorithms that leverage known or learned causal structure to improve out-of-distribution generalisation and provide policy guarantees. Benchmarking & Evaluation : evaluating proposed methods on controlled simulated environments with known causal structure, benchmarked against standard and offline RL baselines. Supervisors Academic Supervisor: Prof. Alessandro Abate, Department of Engineering, University of Cambridge Industrial Co-supervisors: Dr. Miguel Suau and Dr. Alec Edwards, Phaidra The student will be based primarily at the University of Cambridge, with the opportunity to spend time at Phaidra. Funding & Duration This is a fully funded 4-year PhD studentship, expected to start January 2027 , co-funded by Phaidra and administered by the University of Cambridge. Who You Are You are a curious and technically rigorous researcher who wants to work at the intersection of causal inference and sequential decision-making. You are excited by foundational questions with real-world stakes and want your PhD to contribute both to the academic literature and to the practical deployment of intelligent systems. Key Qualifications A first-class or upper second-class honours degree (or equivalent) in Computer Science, Mathematics, Engineering, Statistics, or a related technical field. Strong background in at least one of: reinforcement learning, machine learning, probabilistic modelling, or control theory. Proficiency in Python and standard ML libraries (PyTorch,…