Research Scientist

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.

• 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.
• Own Research work-streams at different levels, depending on seniority.
• 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.
• Foster a nurturing environment for colleagues with less experience in DS / ML / Stats for them to grow and you to mentor.

• Enthusiasm about using machine learning, especially deep learning and/or probabilistic methods, for science and engineering.
• Ability to scope and effectively deliver projects.
• 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.).
• 2 years of experience in a data‑driven role in a professional industry setting (excluding post‑doc positions), with exposure to:
  • building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., Num Py, Sci Py, Pandas, PyTorch, JAX), 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 benchmark models and produce comparable results;
  • writing skills for communication complex technical concepts to peers and non‑peers, tailoring the message for the required audience.
• Publication record in reputable venues that demonstrates mastery in your field, and in particular the domains of interest listed above. Desirable venues include (but not limited to):
  NeurIPS, ICML, ICLR, UAI, AISTATS, AAAI, Siggraph, CVPR or TPAMI/JMLR.

Build what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real‑world impact — and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of…