Principal Research Scientist

Overview

Physics

X is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, Physics

X unlocks new levels of optimization and automation in design, manufacturing, and operations — empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive.

Note:

We are currently recruiting for multiple levels and positions; please apply for the role that best aligns with your skillset and career goals.

• Own Research work-streams at a high level to deliver outcomes.
• Align priorities with problem stakeholders, internal and external.
• Set the technical direction for the stream and apply judgement and taste to drive progress.
• Plan roadmaps with clear milestones for key decisions and outcomes.
• Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap.
• Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment.
• Contribute towards Research group strategy and culture.
• Identify research areas that would be valuable to the company and champion their development, ordering wrt other research objectives.
• Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment.
• Nurture younger colleagues to grow their skillset and guide their professional development.
• The below activities in particular.
• Work closely with our machine learning engineers, simulation engineers, and customers to translate physics and engineering challenges into mathematical problem formulations.
• Build models to predict the behaviour of physical systems using state-of-the-art machine learning and deep learning techniques.
• Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems.
• Collaborate with colleagues beyond the research team to translate your models into production-ready code.
• Communicate your work to others internally and externally as called for in paper publication venues, industry workshops, customer conversations, etc. This will involve writing for academic and non-academic audiences.

• Ability to scope and effectively deliver projects.
• Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Excellent collaboration and communication skills — with teams and customers alike.
• PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, or a related field, with particular expertise in any of the following:
  • operator learning (neural operators), or other probabilistic methods for PDEs;
  • geometric deep learning or other 3D computer vision methods for point-cloud or mesh-structured data;
  • generative models for geometry and spatiotemporal data (VAEs, Diffusion Models, Bayesian non-parametric, scaling to large datasets, etc.).
• Ideally, >4 years of experience in a data-driven role in a professional industry setting, where you have been instrumental in:
  • building machine learning models and pipelines in Python, using common libraries and frameworks (PyTorch / CUDA, ideally with exposure to JAX, Num Py / Sci Py), especially including deep learning applications;
  • developing models for bespoke problem settings that involve high-dimensional data (spatiotemporal, geometric, physical);
  • iterating on network architectures and model structure, tuning and optimising for inductive biases, improved generalisability, and improved performance;
  • combining theoretical reasoning with empirical intuition to guide investigation;
  • formulating and running experiment pipelines to…