丹麦科技大学

Optical Frequency Comb Generation using Machine Learning

项目介绍

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.

Responsibilities and qualifications
The objective of the proposed PhD project is to investigate how machine learning methods can be used for optical frequency comb generation and control. More specifically, the project will investigate inverse system design for low-noise frequency comb generation. 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, device physics 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 industry collaboration in the application of machine learning techniques to optical communication and measurements systems in general. A close collaboration with the University of California Santa Barbara (Prof. John E. Bowers group) is envisioned within the project. 

This Ph.D. project will develop a machine learning framework for optical frequency combs generation. More specifically, we will investigate how machine learning enabled inverse system learning can be used to generate low-noise spectrally broad frequency combs based on micro-resonator rings or other technologies. Moreover, the project will investigate how machine learning can be explored to enable arbitrary-shape spectrum generation in a controlled way – a feature important for the next generation of AI enabled optical networks. The developed combs will then be investigated for optical data signal transmission as well as RF and THz signal generation.

Your work will include research into machine learning methods for inverse system design applied to optical frequency comb generation. Specifically you will focus on the following areas:

  • Physics based numerical models for optical frequency comb generation
  • Multi-layer neural networks for inverse system design
  • Optimization methods
  • Novel training algorithms for multi-layer neural networks
  • Experimental verification of the designs
  • Optical transmission experiments
  • 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, device physics and optical communication systems.

Moreover the candidate shall have additional skills as:

  • Good understanding of laser and device physics
  • Good understanding of digital signal processing (signal analysis, power spectrum estimation, time-series analysis)
  • Good theoretical understanding of linear algebra
  • 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 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.

Assessment
The assessment of the applicants will be made by Associate Professor Darko Zibar and Professor Leif K. Oxenløwe.

We offer
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
Further information may be obtained from Associate Professor Darko Zibar (dazi@fotonik.dtu.dk). The following papers are a good indication of the nature of the project and are good starting point to get familiar with the topic:

  1. D. Zibar, A. M. Rosa Brusin, U. C de Moura, V. Curri, Andrea Carena, “Inverse system design using machine learning: the Raman amplifier case,” Journal of Lightwave Technology, vol. 38, no. 4, pp: 736-753, 2020

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

Application procedure
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.

项目职责

  • Your work will include research into machine learning methods for inverse system design applied to optical frequency comb generation. Specifically you will focus on the following areas:
  • Physics based numerical models for optical frequency comb generation
  • Multi-layer neural networks for inverse system design
  • Optimization methods
  • Novel training algorithms for multi-layer neural networks
  • Experimental verification of the designs
  • Optical transmission experiments
  • Maintenance of the GitHub repository for the developed code
  • Organising and managing joint experiments with the collaboration groups.

录取要求

  • 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, device physics and optical communication systems. Moreover the candidate shall have additional skills as:
  • Good understanding of laser and device physics
  • Good understanding of digital signal processing (signal analysis, power spectrum estimation, time-series analysis)
  • Good theoretical understanding of linear algebra
  • 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
  • Experience with software for version control such as git
  • Experience using MATLAB, Python or similar
  • 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

项目概览

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北欧, 丹麦 所在地点
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截止日期 2021-05-26
丹麦科技大学

院校简介

丹麦技术大学坐落于北欧丹麦王国-哥本哈根大区,由著名物理学家奥斯特于1829年创建。
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联系方式

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