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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Research Scientist - Material Modelling - **Company:** PhysicsX Ltd - **Location:** London, UK - **Salary:** £43,586.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, High Performance Computing, Generative AI, Backend - **Published:** September 14, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5882772726 ## About the Role * 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. 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. ## Description * 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. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Strange New Worlds: shaping the future of the digital age](https://www.wearedevelopers.com/videos/677-strange-new-worlds-shaping-the-future-of-the-digital-age) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Your imaginations is (no longer) the limit: how Generative AI empowers people to be creative](https://www.wearedevelopers.com/videos/741-your-imaginations-is-no-longer-the-limit-how-generative-ai-empowers-people-to-be-creative) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [6 Emerging Technologies We’ll Learn About in 2025](https://www.wearedevelopers.com/magazine/381-6-emerging-technologies-we-ll-learn-about-in-2025)