Workshop on industrial research school

Welcome to a workshop on our planned Industrial Research School in intelligent decision-making and decision support for complex problems in industry.
Date: , kl 13:00 - 15:00
Location: Campus Borlänge
Locale: Room 311

The IRS School is an initiative for Northern Central Sweden and a collaboration between The University of Gävle, Dalarna University and Karlstad University.

We now invite companies to a workshop to inform about the graduate school, talk to the researchers and discuss what collaboration with the graduate school might look like.

The workshop will be held in English. 

Agenda

Presentation of the graduate school

  • Focus areas
  • Benefits for industry
  • Forms of collaboration
  • The way forward

Researchers present opportunities in graduate school

  • Business Intelligence
  • Machine Learning of applied problems
  • Artificial Intelligence and image processing
  • Computational data analytics

Discussion

Summary and next steps

Background

The purpose of our industrial graduate school is to offer training that meets the industry's need for excellence and that strengthens the competitiveness of the participating companies. The graduate school offers the opportunity to develop employees and prepares them for specialist assignments within the company. The graduate school is aimed at industrial development professionals who spend 80% of their working time on company-linked research and development over a five-year period and leads to a PhD. Participants should have at least three years of work experience, be employed by the company and meet qualifications for postgraduate education.

Specialization within the industrial graduate school

The industrial graduate school supports a wide range of research opportunities, ranging from decision-making and decision support for complex problems, over intelligent management of resources and infrastructure, to business intelligence and data scientific questions. Dalarna University focuses on business related questions, artificial intelligence, and data science. In brief, we specialize on everything around the “data analytics” pipeline. It starts with a dialog about the problem, data collection, data curation, and data storage. Followed by advanced data analytics, prediction, modeling, and verification. This process ends with data visualization, critical evaluation, and communication of results. A doctoral student at the end of the education will be able to integrate this data analytical pipeline into almost all industrial processes, critically evaluate and implement new technologies, and communicate problems and answers to all kinds of decision makers.

Contact and registration

Please register before 10 December by sending an email to Yves Rybarczyk or Arend Hintze.

Professor Microdata Analysis
Professor Microdata Analysis
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