PhD Research Intern, Causal Inference and Calibration for Physical Simulators

About Phaidra

Phaidra builds AI agents for AI factories.

As demand for AI grows, data centers need to get more out of the power and infrastructure they already have. Power, cooling, and compute are closely connected, and making them work efficiently is one of the biggest challenges in scaling AI.

Phaidra builds AI agents that help data center operators understand their infrastructure, troubleshoot issues, and optimize how their facilities run. Our agents connect data across IT and OT systems, helping teams find problems faster and use existing resources more effectively. Operators use Phaidra to optimize performance, reliability, and efficiency. From helping technicians get to the root cause of an issue to optimize cooling and power allocation, our work helps data centers maximize tokens per watt.

Phaidra was founded in 2019 by former Google, DeepMind, and Trane engineers. Our team brings deep experience in AI, cybersecurity, mission-critical cooling, data centers, and industrial controls. We believe deploying AI in production demands deep domain expertise. We work closely with our partners to tackle challenges around real-world infrastructure at gigawatt-scale.

Our team has a track record of applying AI to the toughest problems. We achieved superhuman performance with DeepMind’s AlphaGo and cut the energy needed to cool Google’s data centers by 40%. We know AI deeply, and we know how to apply it in production for massive impact.

Phaidra can only achieve its mission if we work well together. Our core values define how we do that: Agency, Velocity, Craft, and Truth. We look for people who embody these values. They help our team deliver excellence consistently and build an engaging, supportive culture.

Phaidra is headquartered in the USA, but we are 100% remote with no physical office. We hire internationally with support from our partner, OysterHR. Our team is spread across the USA, Canada, the UK, Sweden, Spain, Portugal, the Netherlands, Singapore, Australia, and India.

Who You Are

You are a 3rd or 4th year PhD student in machine learning, statistics, causal inference, applied mathematics, engineering or a related field. You think carefully about what a model can and cannot tell us, and you enjoy turning that thinking into practical algorithms and software. You bring a strong background in causality and causal inference, hands-on experience calibrating and evaluating physical simulators, or both.

This is a paid 3-month research internship starting in January or February 2027, ideally based in the UK. You will work closely with dedicated mentors on Phaidra's team.

Phaidra builds differentiable simulators of physical systems such as data centre cooling plants. We use these simulators to answer counterfactual questions, for example what would happen if a setpoint or control policy were changed. Many simulator parameters are hard to pin down from data, and some trade off against each other so that data cannot separate them. This internship asks when that actually matters. The aim is to understand which parameters, or combinations of parameters, must be identified for a given counterfactual answer to be trustworthy, and to build methods that use this to make our simulators more useful and more honest about their confidence.

The exact project will be agreed with your mentors at the start of the internship, and there is room to shape it toward your own interests. Possible directions include counterfactual sensitivity analysis, identifiability and parameter-space structure, uncertainty quantification for counterfactual predictions, and links to experimental design. The primary goal of the internship is a research publication, supported by working algorithms and software.

Responsibilities

• Get up to speed on Phaidra's differentiable simulators and how they are calibrated and used to answer counterfactual questions.

• Review relevant literature in causal inference, identifiability, sensitivity analysis and simulator calibration.

• Formulate a well-defined research question with your mentors and design experiments to study it in simulation.

• Develop algorithms that relate a counterfactual question to the parameters and parameter combinations it depends on.

• Implement these methods as clean, tested research software that integrates with our simulators.

• Evaluate the methods on realistic simulator models and analyse results critically.

• Write up the work as a paper suitable for submission to a leading machine learning, statistics or causality venue.

• Communicate progress through regular check-ins, a mid-point review and a final presentation to the wider team.

Key Qualifications

• Currently in the 3rd or 4th year of a PhD in machine learning, statistics, causal inference, applied mathematics, engineering, or a closely related field.

• A strong understanding of causality or causal inference, or solid experience calibrating and evaluating physical simulators.

• Strong Python skills and experience with a scientific computing or deep learning framework such as PyTorch or JAX.

• Solid grounding in probability, statistics and linear algebra.

• Ability to design experiments, analyse results critically and communicate findings clearly in writing and in person.

• Available to start in January or February 2027 for a 3-month period, and ideally based in the UK.

Preferred Skills & Experience

• A publication record in machine learning, statistics, causality or a related venue.

• Experience with both causal inference and physical simulation.

• Familiarity with identifiability analysis, sensitivity analysis or sloppy models.

• Experience with Bayesian inference, uncertainty quantification or simulation-based inference.

• Familiarity with differentiable simulation or automatic differentiation of dynamical models.

• Experience with optimal or Bayesian experimental design.

• Knowledge of HVAC, data centre cooling or other thermal-fluid systems.

Onboarding

In your first 30 days…

• Complete onboarding and gain access to the codebase and existing simulators.

• Review the relevant simulators, their parameters and how they are currently calibrated.

• Survey the literature relevant to the agreed research direction.

• Agree a written project plan with your mentors, including the research question, planned experiments and target publication venue.

In your first 60 days…

• Deliver a working first version of the core method, applied end to end on at least one simulator.

• Produce initial experimental results that test the main research hypothesis.

• Present interim results at a mid-point review, including what is working, what is not and the plan for the final month.

• Agree with your mentors whether an extension to 6 months makes sense and, if so, what it would cover.

In your first 90 days…

• Complete the main set of experiments, with reproducible results and documented code.

• Deliver a paper draft ready for internal review and submission.

• Present findings to the wider team, covering results, limitations and recommended next steps.

• Hand over code and documentation so the work can be continued, or set out a detailed plan for the extension period if the internship is extended.

General Interview Process

All of our interviews are held via Google Meet, and an active camera connection is required.

1. Meeting with People Operations team member (30 minutes)

2. Meeting with Hiring Manager (60 minutes)

3. Culture fit interview with one of Phaidra’s co-founders (30 minutes)

We use Kula as our hiring platform. During your interview, Kula's AI Notetaker will record a transcript of the meeting to allow the interviewer to focus on the interview, not the note taking.

Base Salary

UK Residents:

• Tier 1 (London): 45 GBP/hour

• Tier 2 (Manchester, Birmingham, Edinburgh, Bristol): 40 GBP/hour

• Tier 3 (Smaller cities / rural areas): 35 GBP/hour

In addition to base salary, this position is eligible for equity. Final salary will be determined based on several factors, including a candidate’s qualifications, skills, competencies, experience, expertise, education and location. In some cases, final compensation may fall outside the posted range. Salary ranges are regularly reviewed and may be adjusted in response to market trends.

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 hiring@phaidra.ai.

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 employee’s 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.