Image Recognition Techniques for Engineering Design Applications

Location: Melbourne, VIC

Duration: 5 months

Proposed start date: August 2019

Project Background

BMT are developing an application to assist the engineering design process which requires large amounts of data to be classified and analysed to extract key information, metrics and trends. This challenging project will require the innovative application of image recognition and deep learning techniques to overcome foreseeable challenges.

Research to be Conducted

Research into image recognition, deep learning and or machine learning techniques to interrogate non-standard document images containing text and engineering design information. Given successful extraction and categorisation of key data elements, data analysis and visualisation techniques will be used to test the validity of extracted data and generate key design insights and information.

Skills Required

We are looking for a PhD student with the following:

ESSENTIAL

  • Specialist image recognition skills and knowledge, both software and hardware aspects.
  • Experience/knowledge of big data analytics, machine learning and deep learning applications.
  • Exceptional communication skills.

DESIRABLE

  • Experience building image recognition AIs, applications and systems.
  • Specific experience with image recognition of documents and similar artefacts.
  • Experience working in multi-disciplinary teams.

Expected Outcomes

Expected outcomes of the project are:

  • Completed and functioning application capable of analysing target documents;
  • Recognition and extraction of key data elements;
  • Categorisation and analysis of key data elements;
  • Development of visualisations and mathematical relationships based on extracted data;
  • Supporting documentation for the resultant application.

Additional Details

The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.

It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner.  The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.

The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

Applications Close

24 July 2019

Reference

APR – 1026