Principal Research Scientist

About us PhysicsX 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, PhysicsX 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, however please only apply for the role that best aligns with your skillset and career goals. What you will do  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.   What you bring to the table 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, NumPy / SciPy), 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.   What we offer 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 alo