Internship - Failure Analysis (AI Modelling and Digitalisation)

The student will be in Failure Analysis (FA) team to undertake and automate FA jobs labelling for cases with different failure modes and hypothesis to enhance Virtual Failure Analysis (VFA) process through AIML development, inclusive leverage ways of large language model.

Job Description

In your new role you will:

• Process Understanding: Understand Failure Analysis process, catalogues and various failure modes / mechanism.

• Tool Application: Understand VFA flow and apply different tools (search engines, RPA).

• Automation Development: Create programming to automate job labelling process.

• Customization: Customize job labelling process for different FA groups.

• Classification: Catergorize failure mode through AIML classification.

• Interface Design: Develop user interface to facilitate Man-machine interaction for above items.

• Deployment Collaboration: To collaborate with IT to deploy end user coded App into operational database.

Internship Learning Outcomes:

• Knowledge Acquisition: Gain knowledge on FA process, failure modes and classification.

• Digital Transformation Insight: Good understanding of future digitalization in FA with Virtual Failure Analysis process and job labelling.

• Tool Exposure: Exposure to use of search engine for topic consolidation, cumulative learning of FA insights.

• Application Development: Develop end user APP for digital automation of FA processes.

• Skill Building: Project management skill. Career Advancement: Gain industry working experience to ease future career.

Your Profile

You are best equipped for this task if you have:

• Educational Background: Undergrad or master in Artificial Intelligent (AI engineering) / Computer Engineering / Data science / Information System / EE / EEE with relevant AI oriented mindset.

• Programming Proficiency: Knowledge in Language programming (eg. Python, Jupiter Notebook) AI & ML.

• Expertise: Knowledge in Artificial Intelligent Machine Learning, Large Language Model (LLM).

• Adaptability & Learning Agility: Interactive and dynamic in learning different failure analysis process flow.

• Preferred intake: Jan 27 - May/ June 27

Contact:

Hillary Woo

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