Internship - AI modeling & Digitalization (Failure Analysis)

Descrizione del lavoro:

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.

Your Role

Key responsibilities in your new role

* Understand Failure Analysis process, catalogues and various failure modes / mechanism
* Understand VFA flow and apply different tools (search engines, RPA)
* Create programming to automate job labelling process
* Customize job labelling process for different FA groups
* Catergorize failure mode through AIML classification
* Develop user interface to facilitate Man-machine interaction for above items
* To collaborate with IT to deploy end user coded App into operational database
Internship Learning Outcomes:
* Knowledge on FA process, failure modes and classification
* Good understanding of future digitalization in FA with Virtual Failure Analysis process and job labelling
* Exposure to use of search engine for topic consolidation, cumulative learning of FA insights
* Develop end user APP for digital automation of FA processes
* Gain project management skills
* Gain industry working experience to ease future career development

Your Profile

Qualifications and skills to help you succeed

* On track to attaining Masters or Bachelors in Artificial Intelligent (AI engineering) / Computer Engineering / Data science / Information System / EE / EEE with relevant AI oriented mindset
* Knowledge in Language programming (eg. Python, Jupiter Notebook) - Knowledge in Artificial Intelligent Machine Learning, Large Language Model (LLM)
* Interactive and dynamic in learning different failure analysis process flow
* Knowledge in Language programming (eg. Python, Jupiter Notebook)
* Knowledge in Artificial Intelligent Machine Learning, Large Language Model (LLM)
* Interactive and dynamic in learning different failure analysis process flow
* Preferred Internship Period: Jun 26 - Dec 26

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