Material Scientist - Thin Film Characterisation in England

At OQC, we are building the quantum computers that will power the next generation of scientific discovery and commercial innovation. Our mission is to put quantum in the hands of humanity and we are looking for exceptional scientists and engineers to help us do it.

The Materials Scientist plays a critical role in advancing the materials and processes that underpin OQC's quantum devices. Partnering closely with senior engineers, you will design and lead in-depth characterization studies, extract insight from complex datasets, and turn experimental results into clear, evidence-based recommendations that directly improve qubit coherence, fabrication precision and device performance.

Working focused on materials R&D, you will combine hands-on experimental expertise with strategic technical judgement, collaborating internally and engaging external characterization facilities when advanced capabilities are required. You will be highly skilled in thin film characterization with strong focus on X-ray thin film characterization techniques.

• Investigate: Apply advanced thin film characterization techniques with a focus on extracting quantitative information on thickness, density, roughness, crystallinity, strain and nanostructure to analyze superconducting device films and interfaces.
• Extract: Use fitting routines and specialized modeling software such as reflectivity fitting and scattering pattern analysis to extract quantitative data from thin film X-ray patterns.
• Develop: and refine custom scan protocols (e.g., grazing incidence, rocking curves, pole figures) to suit complex multilayer stacks.
• Improve: Interpret complex data sets from thin films and multilayer structures to recommend improvements of nanofabrication processes and enhance qubit coherence.
• Document: Create and maintain clear process documentation, experimental plans and progress reports for senior stakeholders.
• Collaborate: Partner with nanofabrication engineers and other material scientists to provide subject matter expertise and data-backed guidance on device performance.

Qualifications

• Strong hands-on experience with advanced thin film X-ray characterization techniques such as XRR, and different modes of XRD.
• Expertise in HRXRD and reciprocal space mapping (RSM) for strain and texture analysis.
• Experience with grazing incidence methods (GIXRD, GISAXS or GIWAXS).
• Proficiency in modeling and fitting X-ray data (e.g., Bruker LEPTOS, GenX, GSAS II, TOPAS, or similar tools).
• Solid understanding of X-ray and electron beam scattering and diffraction theory.
• Experience in diffraction data analysis, including phase identification using crystallographic databases (ICSD, PDF 4 or COD), and interpretation of thin-film diffraction data (texture, strain, epitaxy).
• Experience operating laboratory X-ray diffractometers (e.g., Bruker, Rigaku).
• Programming skills like Python for data analysis and automation.
• Clear written and verbal communication skills, with the ability to produce high-quality technical documentation.
• PhD (or MSc with significant experience) in Physics, Materials Science, or a related discipline.

Nice to haves

• Experience with complementary techniques like SEM, AFM, TEM, EDX or alike.
• Knowledge of thin film growth models and methods.
• Experience with synchrotron-based X-ray techniques.
• Familiarity with Rietveld refinement for polycrystalline materials.
• Experience applying machine learning techniques to materials data.

At OQC, you'll work shoulder to shoulder with world-class scientists, engineers, and technologists who are redefining what's computationally possible. This is a rare opportunity to apply your materials science expertise to genuinely cutting-edge quantum hardware - translating fundamental insight into measurable improvements in device performance and scalability.

If you're motivated by deep scientific challenges, collaborative excellence, and the opportunity to directly impact the future of quantum computing, OQC offers a place where your work will truly matter.