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[AIMLAC CDT Studentship] AI optimization of optoelectronic devices for gravitational-wave detectors

   Cardiff School of Physics and Astronomy

  , , Dr Federico Liberatore  Friday, February 10, 2023  Competition Funded PhD Project (Students Worldwide)

About the Project

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society. 

This project will apply artificial intelligence algorithms to optimize optoelectronic devices to meet the demanding specifications of laser interferometers for detecting gravitational waves and other fundamental physics phenomena.

The typical method of designing such devices is by guessing an initial structure based on previous experience and hand-tuning its parameters to optimize them using models and simulators. It is time-consuming to explore all the design space to improve performance manually. Nevertheless, genetic algorithms, level-set methods, non-linear search algorithms, and other Artificial Intelligence techniques can do it.

If the design space can be optimized automatically, we can enlarge the device space significantly by adding complexity to the device structure. If we have a large enough design space, there will likely be solutions with substantially better performance than small manually-optimized sets.

A relevant example is the work in [1]. By only using dielectrics in manually designed 3D structures, including lenses and mirrors, it is impossible to confine light in a spot below the diffraction limit of light. Nevertheless, in [1], they use a tolerance-constrained topology optimization method to produce an unprecedented only-dielectric cavity that can confine light well below the diffraction limit.

In this Ph.D. studentship, you will employ and modify artificial intelligent algorithms to optimize a tuneable lens on a chip with extremely low loss (<1%) to have adaptative mode matching for optical cavities used in laser interferometers. This multidisciplinary project involves optoelectronic device modeling in Abadia's group, optimization algorithms in Liberatore's group, and quantum-enhanced laser interferometers in Dooley's group. You will use in-house modeling tools and commercial simulators in Abadia's group [2,3] to model tunable lenses to meet the stringent specifications required for gravitational-wave detectors. You will collaborate with Dooley's group [4] at the School of Physics and Astronomy to integrate the lens design to a squeezed-light-enhanced table-top laser interferometer. To improve the lens figure of merit ~100x times, you will drive the modeling tools with an artificial intelligence algorithm to increase the design space and find the fittest solution. You will collaborate with Liberatore's group [5] in the School of Computer Science and Informatics to develop and integrate the artificial intelligence optimization strategy with the modeling tools. 

You will have the opportunity to fabricate the optimized device at the Institute for Compound Semiconductors and test it in a complete instrument in Dooley's group.

To have further information on this role, don't hesitate to get in touch with Dr. Nicolás Abadía at . You can follow us on

Start date: 1st October 2023 

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing provides 4-year, fully funded PhD opportunities across broad research themes: 

  • T1: data from large science facilities (particle physics, astronomy, cosmology) 
  • T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics) 
  • T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms) 

Its partner institutions are Swansea University (lead institution), Aberystwyth University, Bangor University, University of Bristol and Cardiff University. 

Training in AI, high-performance computing (HPC) and high-performance data analytics (HPDA) plays an essential role, as does engagement with external partners, which include large international companies, locally based start-ups and SMEs, and government and Research Council partners. Training will be delivered via cohort activities across the partner institutions. 

Positions are funded for 4 years, including 6-month placements with the external partners. The CDT will recruit 10 positions in 2023. 

The partners include: JD Power UK, ATOS, DSTL, Mobileum, GCHQ, EDF, Amplyfi, DiRAC, Agxio, STFC, NVIDIA, Oracle, QinetiQ, Quantum Foundry, Dwr Cymru, TWI and many more. 

More information, and a description of research projects, can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website. 

How to apply: 

To apply, and for further details please visit the CDT website follow the instructions to apply online.  

This includes an online application for this project at (with a start date of 1st October 2023):

Applicants should submit an application for postgraduate study via the Cardiff University webpages including: 

• your academic CV 

• a personal statement/covering letter 

• two references, at least one of which should be academic 

• Your degree certificates and transcripts to date. 

In the "Research Proposal" section of your application, please specify the project title and supervisors of this project. 

In the funding section, please select that you will not be self-funding and write that the source of funding will be “AIMLAC CDT” 

The deadline for applications for the UKRI CDT Scholarship in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) is mid-February 2023. However, AIMLAC will continue to accept applications until the positions are filled. 

For general enquiries, please contact Roz Toft   


The typical academic requirement is a minimum of a 2:1 physics and astronomy or a relevant discipline. 

Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS) (

Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it). 

For more information on eligibility, please visit the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website 

Funding Notes

The UK Research and Innovation (UKRI) fully funded scholarships cover the full cost of 4 years tuition fees, a UKRI standard stipend of currently £17,668per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.


[1] Albrechtsen, M., Vosoughi Lahijani, B., Christiansen, R.E. et al. Nanometer-scale photon confinement in topology-optimized dielectric cavities. Nat Commun 13, 6281 (2022).
[2] Joe Mahoney, Mingchu Tang, Huiyun Liu, and Nicolás Abadía, "Measurement of the quantum-confined Stark effect in InAs/In(Ga)As quantum dots with p-doped quantum dot barriers," Opt. Express 30, 17730-17738 (2022). -
[3] Md Ghulam Saber, David V. Plant, and Nicolás Abadía, "Broadband all-silicon hybrid plasmonic TM-pass polarizer using bend waveguides," AIP Advances 11, 045219 (2021). -
[4] Sander M. Vermeulen, ..., Katherine L. Dooley and Hartmut Grote. "An Experiment for Observing Quantum Gravity Phenomena using Twin Table-Top 3D Interferometers" Class. Quant.Grav. 38, 085008 (2021).
[5] F. Liberatore et al. "A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in Humanitarian Logistics," Computers & Operations Research 42, (2014) 3-13.

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