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Research Opportunity

Post-doc/Research Associate in Machine Learning: We are seeking applicants with expertise and track record in AI / Machine Learning for a 1 to 2 year position starting around summer 2017 (6-month short term stay is also possible) . We are a leading group in various aspects of signal processing for optical communcations and some of our world-record breaking transmission experiments are regularly covered by media worldwide. Over the past few years, we have applied some machine learning techniques in optical communications and a list of related papers are shown below. We also work on core and contemparory machine learning topics such as discrete latent representations and are exploring topics in the intersection of computation and communications among others. We hereby seek capable researchers who can  independently lead/continue his or her own research agenda in machine learning related area and/or apply machine learning techniques in other areas of engineering such as optics/communications. 


  • F.N. Khan, K. Zhong, W.H. Al-Arashi, C. Yu, C. Lu and A.P.T. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photonics Technology Letters, vol. 28, no. 17, pp. 1886–1889, Sep. 2016.

  • F.N. Khan, Y. Yu, M.C. Tan, W.H. Al-Arashi, C. Yu, A.P.T. Lau, and C. Lu, “Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling,” Optics Express, vol. 23, no. 23, pp. 30337–30346, Nov. 2015.

  • F.N. Khan, Y. Zhou, Q. Sui and A.P.T. Lau, “Non-data-aided joint bit-rate and modulation format identification for next-generation heterogeneous optical networks,” Optical Fiber Technology, vol. 20, no. 2, pp. 68–74, Mar. 2014.

  • F.N. Khan, T.S.R. Shen, Y. Zhou, A.P.T. Lau and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photonics Technology Letters, vol. 24, no. 12, pp. 982–984, Jun. 2012.                                                                  

  • F.N. Khan, Y. Zhou, A.P.T. Lau and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Optics Express, vol. 20, no. 11, pp. 12422–12431, May 2012.

  • T. S. R. Shen and A.P.T. Lau, "Fiber Nonlinearity Compensation Using Extreme Learning Machine for DSP-based Coherent Communication Systems", Paper 8D1_4, The 16th OptoElectronics and Communications Conference (OECC), Taiwan, July 2011.

  • T.S.R. Shen, Q. Sui and A.P.T. Lau, “OSNR Monitoring for PM-QPSK systems with Large Inline Chromatic Dispersion Using Artificial Neural Network Technique,” IEEE Photonics Technology Letters, vol. 24, no. 17, pp. 1564-1567, Sept. 2012.


In addition to our world-class reserach facilities, the world-renowned vibrant, multicutural and metropolitan lifestyle of Hong Kong will surely provide candidates with a unique life experience unparalleled to anywhere else on the planet.


Professor Alan Pak Tao Lau

Room CF 610

Department of Electrical Engineering,

The Hong Kong Polytechnic University

Hung Hom, Kowloon, 

Hong Kong

Tel: (852) 34003346

Email: eeaptlau "AT"


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