About Huawei Research and Development UK Limited

Founded in 1987, Huawei is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. We have 207,000 employees and operate in over 170 countries and regions, serving more than three billion people around the world.

Our vision and mission is to bring digital to every person, home and organization for a fully connected, intelligent world. To this end, we will drive ubiquitous connectivity and promote equal access to networks; bring cloud and artificial intelligence to all four corners of the earth to provide superior computing power where you need it, when you need it; build digital platforms to help all industries and organizations become more agile, efficient, and dynamic; redefine user experience with AI, making it more personalized for people in all aspects of their life, whether they’re at home, in the office, or on the go.

This spirit of innovation has led Huawei to work in close partnership with leading academic institutions in the UK to develop and refine the latest technologies. With a shared commitment to innovation and progress, both parties have worked together to achieve common goals and establish a strong partnership. The partnership between UK and Huawei help to develop the technologies of the future that will transform the way we all communicate, work and live.

For the past 30 years we have maintained an unwavering focus, rejecting shortcuts and easy opportunities that don't align with our core business. With a practical approach to everything we do, we concentrate our efforts and invest patiently to drive technological breakthroughs.

This strategic focus is a reflection of our core values:

• Staying customer-centric,

• Inspiring dedication,

• Persevering,

• Growing by reflection.

Huawei Research and Development UK Limited Overview

Huawei’s vision is a fully connected, intelligent world. To achieve this, we work to inspire passion for basic research around the world. Our combined passion drives development across the global innovation value chain. Huawei has the largest Research and Development organization in the world with 96,000+ employees in research centers around the globe. In the UK, we already have design centers in Cambridge, London, Edinburgh and Ipswich. We continue to explore and define new research directions and new services. We have expanded our collaborations with academic researchers; researched new network architectures, integration of communications and key enabling technologies; and developed the fundamental theories of these technologies. We invite you to join us on this exciting journey and drive your career forward.

Job Summary

Research and develop AI-driven systems for autonomous materials discovery, with focus on crystal structure prediction and property optimization. Design hybrid world models combining symbolic physics simulators with neural surrogates, and implement LLM-guided search algorithms coordinated through reinforcement learning and Bayesian optimization frameworks targeting accelerated discovery of superconductors, catalysts, and functional materials. Bridge the sim-to-real gap by integrating computational chemistry tools (DFT, molecular dynamics) with autonomous laboratory feedback loops for closed-loop experimentation and model refinement.

Key Responsibilities:

• Conduct original research at the intersection of materials science and machine learning, leading to publications in top conferences and journals (e.g., NeurIPS, ICLR, ICML, Nature Materials, JACS, Physical Review).

• Design and implement algorithms for materials discovery using reinforcement learning, Bayesian optimization, and LLM-guided search.

• Develop and validate world models for materials systems, including hybrid symbolic-neural simulators and surrogate models for expensive quantum mechanical calculations.

• Collaborate with domain experts to translate materials science problems into computational frameworks and validate results against experimental data.

• Actively engage with both the ML and materials research communities through publications, open-source contributions, and cross-disciplinary collaboration.

This job description is only an outline of the tasks, responsibilities and outcomes required of the role. The jobholder will carry out any other duties as may be reasonably required by his/her line manager. The job description and personal specification may be reviewed on an ongoing basis in accordance with the changing needs of Huawei Research and Development UK Limited.

Required:

• Master's or PhD (or currently pursuing) in Materials Science, Computational Chemistry, Chemical Physics, or related field, with demonstrated exposure to machine learning applications.

• Strong foundation in crystallography, solid-state chemistry, or computational materials science (DFT, molecular dynamics, structure-property relationships).

• Familiarity with at least one of the following ML areas: Reinforcement learning, Bayesian optimization, world models, or LLM applications.

• Proficiency in Python and experience with scientific computing libraries and at least one ML framework (PyTorch, JAX, or TensorFlow).

• Ability to work in a fast-paced, research-oriented environment bridging materials science and AI.

• Passion for applying AI to accelerate scientific discovery in the physical sciences.

Desired:

• Publications in materials science, chemistry, or physics journals (e.g., Nature Materials, Advanced Materials, JACS, Physical Review) or at ML/AI conferences.

• Hands-on experience with computational chemistry tools.

• Experience with materials databases and high-throughput computational screening.

• Familiarity with graph neural networks for materials or surrogate modeling techniques.

• Knowledge of autonomous laboratories, robotic experimentation, or closed-loop optimization in physical sciences.

• Active GitHub portfolio showcasing materials informatics or AI-for-science projects.

• Understanding of structure prediction methods (genetic algorithms, particle swarm, basin hopping) and their limitations.