CENTRE FOR ADVANCES IN RELIABILITY AND SAFETY LIMITED
Centre for Advances in Reliability and Safety Limited (CAiRS), initiated by The Hong Kong Polytechnic University, 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.
Postdoctoral Fellow (Ref. No.: CAiRS-R19/P5.3 - Data visualization and modeling for processes, products and systems)
[Appointment period: each for thirty-six months]
Research Associate (Ref. No.: CAiRS- R20/P5.3 - Data visualization and modeling for processes, products and systems)
[Appointment period: each for thirty-six months]
Duties
The appointees will assist the project leaders in the research project - “Data Visualization and Modelling for Processes, Products and Systems”. The projects will develop innovative AI methods for representation of multi-dimensional data and images, in order to facilitate interpretation, modeling, expert checking of validity, educational use and solving an industrial problem.
The Postdoctoral Fellow appointee will be required to:
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conceptualize industrial pain point/motivation to a good structure AI (machine learning/deep learning/reinforcement learning) reliability and safety research solution associated with the latest technologies such as cloud, 5G, IoT...etc.
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work with talents from industrial collaborators and other internal researchers on the research subject matter with project proposal, project report, and communicate internally and externally the research plan;
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leverage and share research expertise and skills on a daily teamwork basis. The appointee has genuine interest in AI (machine/deep learning) technology;
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prepare and publish relevant research papers in high-tier peer-reviewed journals.
The Research Associate appointee will be required to:
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plan industrial pain point/motivation to a good structure AI research;
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team up with a group of postdoctoral researchers with specific expertise to implement the research work and experiment;
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drive to reach the reliability and safety objectives within scheduled timeline;
- report project work issues and support devising of project proposal and report.
Qualifications
Applicants for the post of Postdoctoral Fellow should have a doctoral degree in Data Science, Computer Science, Engineering, Mathematics or an equivalent qualification in a STEM related field. They should also have strong research/analytical skill with interests in the latest technologies, e.g. artificial intelligence, big data, cloud, 5G, IoT, blockchain...etc.
Applicants for the post of Research Associate should have a good honours degree or above in Data Science, Computing Science, Engineering, Mathematics or related field coupled with some industrial experience of the latest technologies.
For both posts, applicants should also have:
- interest in the latest technology applied research, eager to learn new methodology in applying AI to solve existing industrial reliability and/or safety problem. Self-drive, exploratory attitude to follow the research scope and direction.
- research capability to solve a practical problem with AI expertise, passion and plan to execution, design/conduct experiments and result driven.
- paper publication, patent application, commercialization experience are distinguished advantages
- proficiency in various operating system (Window/Linux/Ubuntu) and programming languages for supercomputer (e.g. C++, Java, Python/Pytorch, CUDA…);
- good interpersonal, presentation, communication skills and command of written and spoken English.
Preference will be given to those with research experience in machine/deep learning and data science.
Salary:
- The maximum monthly salary allowance is HK$23,000 for research talents with a master degree and HK$35,000 for those with a doctoral degree.
- An additional monthly living allowance of HK$10,000 will be provided to research talents with a doctoral degree.
What you enjoy at CAiRS:
We offer highly competitive salary commensurate with qualifications and experience, in addition to the following benefits:
- 5-day work
- Staff's entitlement to paid annual leave will increase progressively according to the Centre’s guideline /Flexible working hours (for researchers)
- Medical & dental insurance
- Excellent and friendly office environment (newly renovated and next to sea shore)
- Free drinks and snacks in office
What you enjoy at Science Park:
- Shuttle bus to Science Park from various locations in HK
- Recreational facilities in Science Park, e.g. gym and swimming pool
- Can apply for studio apartment in InnoCell (a smart living and co-creation space designed for I&T talents in Science Park) subject to availability and eligibility
Application Guidelines
- Please return the completed application form, together with a detailed curriculum vitae, to the CAiRS by email to careers@cairs.hk
- Application form can be downloaded from here.
- The CAiRS reserves the right to fill or not to fill the position. The personal data in relation to your application will be used by CAiRS to assess your suitability for assuming the position you are applying for, and to determine the remuneration and benefits package, if applicable.
- Please read the “Personal Information Collection Statement for Recruitment” before completing the application form.
- The CAiRS is an equal opportunity employer committed to diversity and inclusivity. All qualified applicants will receive consideration for employment without regard to gender, ethnicity, nationality, family status or physical or mental disabilities.
- Applicants who are not invited to an interview within two months of the closing date should consider their applications unsuccessful.