Engineering the Next Generation of Broadband Terahertz Technologies

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Supervisor – Professor Aleksandar

The UQ team is currently working on an exciting new sensing technology based on quantum cascade lasers (QCLs) in collaboration with high profile teams at universities in Europe, including Ecole Normale Paris and the University of Leeds.  This position will be based out of UQ and work  closely with researchers here and with our international collaborators.

Terahertz (THz) QCLs have emerged as a premier compact source of high-power radiation in the THz spectral range. Combining the QCL illumination source with laser-feedback interferometry (LFI) — an effective self-detection scheme — provides a high-speed high-sensitivity detection mechanism which inherently supresses unwanted background radiation. Operating in the THz (~0.1–10 THz) and enjoying the high output power of QCLs, this THz sensing scheme enjoys has been successfully employed for a range of imaging and sensing applications. The other main technology platform is time domain spectroscopy (TDS) which has the distinct advantage of broadband operation, which permits its use in spectroscopy, but suffers from low power at THz frequencies >~2 THz.

Currently, there is no technological platform that enjoys high power broadband operation at THz frequencies > 2 THz. This aim of this project investigate the generation and self-detection of ultra-short THz pulses at high repetition rate in model and experiment, and to demonstrate the potential of this approach to sensing and imaging technologies.

In this PhD project, the successful candidate will work on model development for pulsed, coupled cavity (CC) THz QCL dynamics under optical feedback and mode-locked (ML) THz QCL dynamics under retro-injection.

The major focus will be on creation of effective signal processing algorithms for image formation based on  (i) modelled signals and +(ii) experimentally-acquired signals.

Preferred educational background

Applications will be judged on a competitive basis taking into account the applicant's previous academic record, publication record, honours and awards, and employment history.

A working knowledge of physics, electrical engineering and signal processing would be of benefit to someone working on this project.

The applicant will demonstrate academic achievement in the field(s) of mathematics/statistics and the potential for scholastic success.

A background or knowledge of programming (e.g. Matlab, Python) and signal processing is highly desirable.

*The successful candidate must commence by Research Quarter 2, 2021. You should apply at least 3 months prior to the research quarter commencement date. International applicants may need to apply much earlier for visa reasons.

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