Applications are invited for a 3-year PhD position funded by the Silicon Photonics for Optical Communications (SPOC) centre of excellence at DTU Fotonik. The centre was established in 2015 and is funded by the Danish National Research Foundation. The goal of the SPOC centre is to advance the state-of-the-art in ultra-high capacity optical communications and this PhD project will be at the heart of this activity.
The objective of the proposed PhD project project is to develop novel machine learning based methods for noise characterization of lasers and optical frequency comb. The start date is flexible but preferably as soon as possible. The project is interdisciplinary and will cover topics within the field of laser physics, machine learning, quantum optics and optical communication. The project will be carried out in Machine Learning in Photonic Systems (M-LiPS) group. The group has a strong track record and and industry collaboration in the application of machine learning techniques to optical communication and measurements systems in general. A close collaboration with NKT Photonics (leaders in ultra-low noise laser sources) and the University of California Santa Barbara (Prof. John E. Bowers group) is envisioned within the project.
Responsibilities and qualifications
This Ph.D. project will explore the latest advances within machine learning to enable ultra -broadband and -sensitive noise characterization of laser sources and frequency combs. A machine learning based framework for joint tracking of amplitude and phase noise will be developed. The framework will rely on Bayesian filtering which offers record sensitivity and operation at to the quantum limit. The project will also focus on learning the corresponding state-space models for tracking amplitude and phase noise. The focus will be on low-complexity solutions that are feasible for real-time implementation. The project will cover both algorithm development as well as experimental implementations.
Your work will includes research into novel methods for noise characterization of lasers and frequency combs. Specifically you will focus on the following areas:
- Bayesian filtering framework for joint tracking of amplitude and phase noise of lasers and frequency combs
- Multi-layer neural networks for learning the evolution of amplitude and phase noise from the measurements data
- Relating the amplitude-phase noise correlation matrices to comb’s physical parameters such as timing jitter, carrier envelope frequency offset and power supply noise
- Building experimental set-ups for noise characterization of laser and frequency combs
- Maintenance of the GitHub repository for the developed code
- Organising and managing joint experiments with the collaboration groups
You must have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree.
You are expected to have experience with laser physics, machine learning, Bayesian filtering and optical communication systems.
Moreover the candidate shall have additional skills as:
- Good understanding of adaptive filtering techniques (Kalman and Wiener filtering)
- Good understanding of digital signal processing (signal analysis, power spectrum estimation, time-series analysis)
- Good theoretical understanding of linear algebra with special focus on singular value and eigenvalue decomposition methods
- Good understanding of numerical methods for optimization
- Experience with machine learning techniques (expectation maximization algorithms, neural networks, Guassian processes, etc.)
- Experience using MATLAB, Python or similar
- Experience with software for version control such as git
- Ability to work independently, to plan and carry out complicated tasks
- Good communication skills in English, both written and spoken
- Innovative skills and the ability to generate new ideas
Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide.
The assessment of the applicants will be made by Associate Professor Darko Zibar and Professor Leif K. Oxenløwe.
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.
Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.
You can read more about career paths at DTU here.
Further information may be obtained from Associate Professor Darko Zibar (email@example.com). The following papers are a good indication of the nature of the project and are good starting point to get familiar with the topic:
- G. Brajato, L. Lundberg, V. Torres-Company, M. Karlsson, Darko Zibar, “Bayesian filtering framework for noise characterization of frequency combs,” Optics Express, vol. 28, no. 9, pp. 13949-13964, 2020
- D. Zibar, H. –M. Chin, Y. Tong, N. Jain, J. Guo, L. Chang, T. Gehring, J. E. Bowers, U. L. Andersen, “Highly-sensitive phase and frequency noise measurement technique using Bayesian filtering,” IEEE Photonics Technology Letters, vol. 31, no. 23, pp: 1866 – 1869, 2019
You can read more about DTU Fotonik at www.fotonik.dtu.dk.
If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.
Your complete online application must be submitted no later than 26 May 2021 (Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link “Apply online”, fill out the online application form, and attach all your materials in English in one PDF file. The file must include:
- A letter motivating the application (cover letter)
- Curriculum vitae
- Grade transcripts and BSc/MSc diploma
- Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here)
You may apply prior to obtaining your master’s degree but cannot begin before having received it.
All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.