Postdoctoral Fellow / Research Associate - Computer Vision
[Appointment period: thirty-six months]

Duties 

The appointees will report to the Project Leader and the Centre Director or his designate (currently Program Manager), and perform research and related work in the development of machine-learning-based anomaly detection algorithms and surveillance systems for health monitoring of modern electronic systems.

(Ref No. R17-R18/P1.1)
P1.1: Informative feature discovery and selection

(Ref No. R17-R18/P1.2)
P1.2: Early detection of degradation in electronic interconnects 

(Ref No. R17-R18/P1.3)
P1.3:  Anomaly detection for systems under indeterminate operating conditions

For the post of Postdoctoral Fellow, the appointee will be required to:

(a) prepare literature review on machine vision methods on the anomaly detection of electronic assemblies;

(b) design required machine vision systems as required in the health monitoring of modern electronic systems;

(c) develop machine vision and learning algorithms for the anomaly detection and surveillance on the heath of different components mounted on the electronic assemblies;

(d) prepare and publish relevant research papers in high-tier peer-reviewed journals;

(e) perform any other duties as assigned by the Centre Director or his delegates.

For the post of Research Associate, the appointee will be required to:

(a) conduct literature search on the machine vision methods on the anomaly detection of electronic assemblies;

(b) set up required machine vision systems as required in health monitoring of electronic systems;

(c) study machine vision and learning algorithms for the anomaly detection and surveillance on health of different components mounted on the electronic assemblies;

(d) prepare relevant technical reports; and

(e) perform any other duties as assigned by the Centre Director or his delegates.

 
Qualifications 

Applicants for the post of Postdoctoral Fellow should have a doctoral degree in Electrical/Electronic Engineering, or an equivalent qualification in a related field. They shoud have a good publication record. 

Applicants for the post of Research Associate should have a master's degree in Electrical/Electronic Engineering or a related field.

For both posts, applicants should also have:

(a) good understanding of computer vision, pattern recognition and machine learning; and

(b) experience in algorithm development and implementation; and

(c) Proficient in programming languages including Python, C/C++.

(d) Familiar with deep learning frameworks such as Tensorflow or Caffe.

(e) good interpersonal and communication skills; and a good command of written and spoken English.

*** Fresh graduates are welcome.

Remuneration

A highly competitive remuneration package will be offered. Applicants should state their current an expected salary in the application.

Application

1) Please download our job application form from "https://www.cairs.hk/assets/files/Careers/Job_Application_Form.pdf" 

2) Please return the completed application form, together with a detailed curriculum vitae, to the CAiRS by email to us or clicking "apply now"

Deadline for application
Recruitment will continue until the positions are filled.

Centre for Advances in Reliability and Safety Limited (CAiRS), initiated by The Hong Kong Polytechnic University, is a research centre admitted under [email protected] cluster and is established in 2020 with its operation located in the Hong Kong Science Park, New Territories, Hong Kong. The mission of CAiRS is to bridge academic and industrial counterparts to introduce and implement artificial intelligence methods and prognostic techniques to advance reliability and safety. The goal of the Centre is to improve reliability and safety of critical components and devices, products, systems and sub-systems designed, commissioned and/or manufactured by Hong Kong companies and enterprises. More information about the company can be found at http://www.cairs.hk.