Research Fellow - All-Optical Magnetic Switching

Queen's University Belfast

About the job:

To be a highly productive, ambitious and collaborative member of the EPSRC project Deterministic Recording via All-Optical Magnetic Switching (DREAMS) spanning Queen’s University Belfast (materials synthesis & device test), University of Exeter (ultrafast optical characterisation), University of York (theory & simulation) and Seagate Technology (device fabrication & test). Based at Seagate Technology (Springtown), you will lead microfabrication and metrology to realise device structures from pre-deposited magnetic wafers, including design and patterning of pillars and Hall bars, process integration, and electrical/optical metrology to evaluate stacks for all-optical magnetic switching (AOS).

You will plan and run cleanroom process flows end-to-end—from mask design and lithography, through etch, metallisation, passivation, and wafer-level test—working closely with DREAMS partners to align device architectures with project work-packages. This post requires executing independent research in device fabrication and metrology, operating within a complex team, and delivering high-quality, timely results to accelerate discovery and device translation.

About the person:

The successful candidate will:

• Normally have or be about to obtain a relevant PhD in Physics, Materials Science, Electrical/Electronic Engineering, or a closely related field with demonstrable specialism in microfabrication, magnetics/spintronics, or nanoelectronic devices.
• Demonstrate relevant research experience to include:
  • Conducting device fabrication with lithography (photo/e-beam), lift-off and etch (IBE/RIE);
  • Patterning of magnetic multilayers into pillars/Hall bars;
  • Magneto transport measurements (e.g. four-point probe, Hall, AMR/PMR/AHE, CPP-GMR);
  • Proven ability to carry out rigorous analyses, critical evaluations and interpretation of experimental data;
  • Teamworking experience;
  • Publication record commensurate with career stage.
• Demonstrate proficiency with data analysis tools (e.g., Python/Matlab/Origin).
• Have the ability to assess and organise resources; meticulous attention to detail; commitment to reproducibility and quality; ability to work to deadlines in a fast-paced cleanroom environment.

To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information document on our website.