Themes

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The SfTI Challenge is based upon four areas of capability, or Themes. These are broad, multidisciplinary research topics that potentially span many different applications and industry sectors.

The Themes are:

  • Vision Mātauranga
  • Materials, manufacturing and design
  • Sensors, robotics and automation
  • Data science & digital technologies

 

Vision Mātauranga - Whakakitenga Mātauranga

Kāhui Kaihautu – Te Taka Keegan.

Theme leader – Katharina Ruckstuhl.

Vision Mātauranga guides researchers on how to integrate western science with mātauranga Māori (knowledge) to explore new opportunities to build a prosperous, technology-driven economy.

 

Materials, manufacturing and design - Ngā matū, te whakanao me te hoahoa

Theme leader – Don Cleland.

New Zealand has a small, vibrant hi-technology processing and manufacturing sector. This Theme aims to advance the sector’s reputation as a leader in smart, green, manufacturing processes and materials.

These lead to products, services and processes that position New Zealand’s brand well in premium export markets.

Research includes:

  • advancing knowledge of existing materials – and developing new ones – that build on New Zealand’s unique expertise, flora and fauna
  • improving manufacturing and processing technologies to enhance sustainability and/or produce new products
  • using advanced design thinking at all stages of product, service, and process development.

 

Sensors, robotics and automation - Ngā Pūoko, karetao me te aunoatanga

Theme leader – Bruce MacDonald.

This Theme aims to develop robotics and automation for use in a  wide range of products and applications.

The focus is on cost reduction, improved efficiencies, improved safety in dangerous environments, and undertaking tasks which wouldn’t be economically viable otherwise.

 

Data science and digital technologies - Hangarau Mōhiohio, te tātari raraunga me te whakatauira

Theme leader – Stephen MacDonell.

This theme was renamed in 2019 from IT, Data Analytics and Modelling in acknowledgment of increased emphasis on data science, machine learning and artificial intelligence. It aims to develop innovative algorithms, models, methods, tools and practices that could underpin new or enhanced business processes, hardware components, and systems and software applications to enable industry to customise and turn these technologies into economically valuable products and services. While some of the research conducted under this theme will involve the novel use of existing technologies to transform a range of high-tech sectors, other research will seek to develop entirely new data science and digital technology innovations.

 

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