PhD Research Intern, Scientific Library of HVAC 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 mechanical engineering, applied mathematics, machine learning, reinforcement learning, control or a related field. You enjoy building tools that other researchers will use, and putting them to work on real problems. You are comfortable moving between physical modeling, learning-based control, numerical methods and clean library design.

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.

The simulation team at Phaidra is developing a library of scientific HVAC simulators. This covers individual components such as chillers, heat exchangers and pumps, as well as complete systems and loops such as cooling distribution units (CDUs) and chiller plants.

The first objective of the project will focus on contributing to that library, for example by extending its components, improving its computational efficiency or enhancing reusability and composability of its components.

The second objective of the project will consist of a practical application, which should ideally focus on exploiting the library’s batched simulation and differentiability capabilities.

Example applications include:

• A control policy that exploits the differentiability of the simulators, for example gradient-based policy learning or trajectory optimization.

• Faster simulation through model order reduction or surrogate modeling, validated against the full simulators.

• Uncertainty quantification in simulation, propagating parameter and input uncertainty through rollouts to support counterfactual analysis.

The exact scope will be agreed with your mentors and may evolve during the internship. You are free to steer the application toward your own interests and background. A paper is a possible further deliverable, for example for the NeurIPS Datasets and Benchmarks track.

Responsibilities

• Get up to speed on the existing library component and system models and the simulation code around them.

• Identify current gaps in the library and improvements that could be done to the library, for example by adding components and systems, by consolidating interfaces or adding reference control baselines.

• Design and build the practical application on the library that showcases its batching and differentiability capabilities.

• Evaluate the application rigorously against relevant baselines, for example non-differentiable controllers, full-fidelity simulators or sampling-based methods.

• Write tests, examples and documentation so others can use and build on your contributions.

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

• If the team decides to publish the library open-source, help prepare it for release.

• If the work leads to a publication, collaborate with your mentors on the writeup.

Key Qualifications

• Currently in the 3rd or 4th year of a PhD in any engineering field, reinforcement learning, control, applied mathematics, or a closely related field.

• Strong Python skills and hands-on experience with PyTorch/JAX, including automatic differentiation and batched, vectorised computation.

• Experience with reinforcement learning, optimal control or related decision-making methods.

• Experience building research software that others use, with attention to API design, testing and documentation.

• Familiarity with modelling dynamic physical systems, such as ODE models, heat transfer or fluid systems.

• 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

• Experience contributing to or maintaining open-source projects.

• Experience designing benchmarks, datasets or evaluation suites for machine learning research.

• Experience with differentiable simulation, gradient-based policy optimization or trajectory optimization.

• Experience with model order reduction, surrogate modeling or neural operators for physical systems.

• Experience with uncertainty quantification, sensitivity analysis or counterfactual analysis.

• Knowledgeable in HVAC, data centre cooling or other thermal-fluid systems.

• Experience with model predictive control or classical control baselines.

• A publication record in engineering, machine learning, reinforcement learning or control venues.

Onboarding

In your first 30 days…

• Complete onboarding and gain access to the codebase and existing simulation models.

• Review the existing component and system models, and document their interfaces, assumptions and gaps.

• Survey existing simulation benchmarks and RL environment suites for datacenters and HVAC control, and the literature relevant to the chosen application.

• Agree a written project plan with your mentors, including the planned library contributions, the chosen application, success metrics and the target final deliverable.

In your first 60 days…

• Deliver a first set of contributions to the library, as agreed with your mentors.

• Deliver a first working version of the application, using batched rollouts and gradient-based sensitivities on at least one system.

• 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 and merge your library contributions, with tests and documentation in place.

• Complete the evaluation of the application against baselines, with results that are reproducible from the repository.

• Deliver the agreed final output: the library contributions and the application, written up as a paper or technical report draft.

• 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 Research Engineering (45 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.