Research Assistant/Associate CUHK (Fixed Term)

Fixed-term: The funds for this post are available for 36 months in the first instance.

We are looking for a Post-Doctoral Research Associate (PDRA), with a PhD in Physics, Materials Science or Engineering and a strong background in experimental magnetism. The position, based within Prof Jason Robinson's research group at the Department of Materials Science & Metallurgy, is part of the project "Coupled Magnetic Hybrids for Advanced Quantum Technologies", a collaboration with the City University of Hong Kong (CUHK). The aim of the role is to explore how nanomagnetic elements in proximity structures with spin-orbit-coupled interfaces and superconductors can be used to generate and control unconventional superconducting states. In particular, the project aims to realise and manipulate electron pairing to enable spin-polarised charge and pure-spin supercurrents. The role will involve developing and characterising hybrid superconducting devices with ferromagnets and antiferromagnets, with a focus on materials engineering, device fabrication, and low-temperature electrical transport measurements to probe spin-dependent superconducting phenomena. The role holder is expected to visit our collaborators CUHK and work with colleagues in their Department of Physics. The successful candidate will bring hands-on experience in physical vapour deposition of thin films, nanodevice fabrication, and low-temperature electrical and magnetic measurements. With a strong track record of peer-reviewed publications, this individual will be able to demonstrate professional expertise and advanced problem-solving skills. Outstanding written and oral communication skills are essential, along with a proven ability to work effectively within multidisciplinary teams. The candidate must also show a capacity to plan and manage complex research projects involving multiple interdependent components. Experience in programming (python) and applying AI-assisted technologies to streamline research and analysis workflows is highly desirable. Strong analytical capabilities and proficiency in the quantitative analysis of large datasets are also required.

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Two supporting documents are required with the application: (1) a CV, and (2) a one-page cover letter/research statement.

Enquiries regarding the application process may be directed to Nichola Daily, email nd241@cam.ac.uk

Please quote reference LJ46545 on your application and in any correspondence about this vacancy.

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