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 theyve 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 onindustrialapplications which tend to be well-sensorized with measurable KPIs perfect for reinforcement learning.
• We enable domain experts (our users) toconfigurethe AI controlsystems (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 withDeepMinds AlphaGo to reducing the energy required to coolGoogles Data Centersby 40% we deeply understand AI and how to apply it in production for massive impact.

Phaidras ability to achieve its mission is determined by our ability to work together as defined by our core values:TransparencyCollaborationOperational ExcellenceOwnership andEmpathy.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 partnerOysterHR. 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 worlds 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:

1. Theoretical Foundations: formalising policy learning from biased small datasets through a causal lens; characterising how confounding and mediators affect offline RL.

2. Algorithm Development: building RL algorithms that leverage known or learned causal structure to improve out-of-distribution generalisation and provide policy guarantees.

3. 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 NumPy SciPy scikit-learn).
• Clear scientific writing skills and the ability to communicate research to both academic and applied audiences.
• Eligibility to study at the University of Cambridge (international students welcome; English language requirements apply).

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 Phaidras careers portal at You will be asked to submit a CV and a short cover letter describing your research interests and motivation.
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 and reference this studentship in your application. You are also encouraged to email Prof. Abate directly at with your CV and a one-page statement of research interest.

Both applications must be submitted. We encourage you to apply as soon as possible. Applications close 30 July 2026.

The studentship is expected to start January 2027.

Interview Process

1. Initial conversation with Phaidra People Operations (30 minutes)
2. Technical and research discussion with an industrial supervisor (60 minutes)
3. Meeting with the academic supervisor (60 minutes)

Benefits & Perks

• Fast-paced team-oriented environment where your work directly shapes the companys direction.
• We are a 100% remote company.
• Competitive compensation & meaningful equity.
• Outsized responsibilities & professional development.
• Training is foundational; functional customer immersion and development training.
• Medical dental and vision insurance (exact benefits vary by region).
• Unlimited paid time off with a required minimum of 20 days per year.
• Paid parental leave (exact benefits vary by region).
• Flexible stipends to support your workspace well-being and continued professional development.
• Company MacBook.

Please note: Not all of Phaidras benefits and perks listed above apply to temporary employees such as interns.

On being Remote

We take a thoughtful and intentional approach to remote collaboration. Inspired by pioneers like GitLab we embrace proven best practices to foster an exceptional remote work environment. Our culture is documentation-first and we prioritize asynchronous communication to support focus and flexibility across time zones. While we value independence we stay closely connected through tools like Slack and video conferencing. Weekly all-hands meetings help us align and build strong relationships and we regularly host virtual team-building activities and social events to maintain a sense of camaraderie.

Equal Opportunity Employment

Phaidra is an Equal Opportunity Employer; employment with Phaidra is governed on the basis of merit competence and qualifications and will not be influenced in any manner by race color religion gender national origin/ethnicity veteran status disability status age sexual orientation gender identity marital status mental or physical disability or any other legally protected status. We welcome diversity and strive to maintain an inclusive environment for all employees. If you need assistance with completing the application process please contact us at.

E-Verify Notice

Phaidra participates in E-Verify an employment authorization database provided through the U.S. Department of Homeland Security (DHS) and Social Security Administration (SSA). As required by law we will provide the SSA and if necessary the DHS with information from each new employees Form I-9 to confirm work authorization for those residing in the United States.

Additional information about E-Verify can be found here.

#LI-Remote

To be considered for any position at Phaidra you must submit an online application. This role will remain open until it is filled.

Phaidra only hires individuals who are legally authorized to work in the specified location(s) above. We do not provide employment sponsorship. Candidates requiring visa sponsorship either now or in the future are not eligible for hire.

Candidates who advance beyond the initial screening stage will be required to sign a Non-Disclosure Agreement (NDA) in order to continue through the interview process.

All employment offers are contingent upon successful completion of employment authorization verification and applicable background checks in accordance with local laws and company policies.

WE DO NOT ACCEPT APPLICATIONS FROM RECRUITERS.