2026/2027 PhD Residency - Scientific ML (SciML) and Multiscale Physics (Early Stage Project)

X is a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people.

X9 is the employer — EngRadar is a job radar, not a recruiter. We track this posting from their own careers page and send you straight there; we never handle applications or CVs.

Mountain View, CA Posted 25d ago

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How you will make 10x impact:

Combining fluid dynamics and machine learning to solve some of the most important problems in materials discovery and manufacturing
Working at the absolute forefront of simulation + experimentation, replacing computationally heavy, brute-force forward solvers with lean, physics-informed architectures

This project is focused on building advanced simulations and models along with custom equipment and processes to accelerate materials discovery and other critical manufacturing challenges.

Location: X's headquarters in Mountain View, CA
Start Date(s): Year-round rolling basis
Duration: a flexible 6 mo. to 1 year program based on project team needs and your availability

Throughout your AI Residency you can expect:

To be embedded into one of our confidential or public X projects
To get paid competitively and receive benefits
To be a part of a lively community of AI and ML Residents
To attend tech-talks with AI leaders from across X

What you should have:

Must be actively enrolled in a PhD program
First-principles understanding of multiscale physics from continuous fluid dynamics to discrete particle modeling
Proven expertise in Scientific Machine Learning (SciML), specifically the ability to parameterize unresolved or complex physics (e.g., phase coupling, closure terms) into autodifferentiable physics solvers and implement, optimize, and scale relevant SciML frameworks
Practical familiarity with laboratory environments, physical instrumentation, or a strong, demonstrated inclination to tinker and build

It’d be great if you also had these:

Direct experience with PINNs, Neural Operators, and traditional ML
Proficiency in high-performance simulation tools (e.g., COMSOL, OpenFOAM)
Track record of applying machine learning to complex physical systems (e.g., turbulence, multiphase flow, or inverse design)

Additional public information:

The US base salary range for this position is $100,000 - $157,000 + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include benefits.

Posted by X9 on their own careers page — you apply directly, no recruiter in between. View original / apply →