Research Scientist - Material Modelling

Back to jobsResearch Scientist - Material Modelling London, United KingdomApplyAbout 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  Work closely with a multi-disciplinary team ranging form computational chemists with varied domain expertise to machine learning engineers to employ and advance the state-of-the-art machine learning techniques for solving a variety of problems in materials. Develop and apply machine learning interatomic potentials (MLIPs) to model atomistic systems, leveraging and extending state-of-the-art frameworks and benchmark datasets. Design and run experiments on large-scale chemistry and materials datasets, iterating on model architectures to improve accuracy, transferability, and generalisation across systems. Own research workstreams at different levels of scope, depending on seniority, from model development through to evaluation on real-world materials problems. Collaborate with the broader research team to ensure your models are robust, reproducible, and translatable into production-ready pipelines. Work on high-performance computing infrastructure to handle the scale and complexity of atomistic simulations and generative modelling tasks. Communicate your work internally and externally — through paper publications, industry workshops, and customer conversations — tailoring the message for both academic and non-academic audiences. Mentor colleagues with less experience in computational chemistry or materials ML as the team grows.   What you bring to the table Enthusiasm for applying machine learning to real-world materials science and computational chemistry challenges, with a genuine interest in seeing your research have industry impact. Ability to scope and effectively deliver research projects, balancing rigour with pragmatism. Strong problem-solving skills and the ability to move quickly from a materials or chemistry challenge to a tractable computational formulation. Excellent collaboration and communication skills — with research colleagues, engineers, and customers alike. PhD in computational chemistry, physics, materials science or a closely related field. Hands-on experience in using and fine-tuning at least one MLIP backend such as MACE or FAIRChem/OCP and their integration into larger computational framework. Direct experience with established chemistry and materials benchmark datasets, such as OC20, OC22, or the Materials Project. Proficiency in Python and experience working in high-performance computing environments as well as experience in contributing towards a large multi-module codebase Experience with generative models preferably applied to molecular or materials systems. 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 alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating...