PhD Studentship: Causal Reinforcement Learning - Phaidra

About Phaidra

Phaidra is building the future of industrial automation. We create AI-powered control systems for the industrial sector, enabling facilities to automatically learn and improve over time.

• We use reinforcement learning algorithms to convert raw sensor data into high-value actions and decisions.
• We focus on well-sensorized industrial applications with measurable KPIs.
• We enable domain experts to configure AI control systems without writing code.

Phaidra is based in the USA, but we are 100% remote with no physical office, operating with a documentation-first, asynchronous culture.

About the Project

Phaidra builds autonomous AI control systems for data centre and industrial infrastructure, deploying reinforcement learning in production on complex physical systems. This studentship is an opportunity to work on foundational RL research while staying grounded in real-world challenges.

This PhD project tackles the limitation of agents exploiting spurious correlations 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 project will proceed in three phases:

1. Theoretical Foundations: Formalising policy learning from biased, small datasets through a causal lens.
2. Algorithm Development: Building RL algorithms that leverage known or learned causal structure to improve out-of-distribution generalisation.
3. Benchmarking & Evaluation: Evaluating methods on controlled simulated environments with known causal structure.

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.

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, NumPy, SciPy, scikit-learn).
• Clear scientific writing skills and the ability to communicate research to academic and applied audiences.
• Eligibility to study at the University of Cambridge (international students welcome).

Preferred Skills & Experience

• Familiarity with causal inference, causal graphical models, or structural equation models.
• Prior research experience (undergraduate thesis, MSc dissertation, research internship, or publications).
• Experience with offline RL, batch RL, or safe RL.
• Exposure to applying ML to real-world physical or industrial systems.

How to Apply

There are two parallel steps, both required:

1. Apply through Phaidra's careers portal at https://job-boards.greenhouse.io/phaidra with a CV and short cover letter.
2. Apply to the University of Cambridge through the postgraduate application portal for the PhD in Engineering programme. Name Prof. Alessandro Abate as your proposed supervisor.

Applications close 30 July 2026.