participants at "spikning" (nailing) of dissertation at the University library in Växjö

Companies wanted to feature doctoral students in an industrial graduate school on big data

Linnaeus University is currently building a graduate school allowing professionals to pursue an education and conduct research in big data related to industry, leading to a licentiate or PhD degree. Would you like to join our funding proposal to the Knowledge Foundation's (KK) industrial graduate schools programme?

Background

Linnaeus University has a prominent research centre on big data, Linnaeus University Centre for Data Intensive Applications and Sciences (DISA). The university also offers free, industry-tailored courses for professionals in the digital sector that are partly financed by the graduate professional development programme of the Knowledge Foundation.

Industrial graduate schools is a complementary programme from the Knowledge Foundation that enables the development of research-linked courses and postgraduate education in close collaboration between academic environments and companies. Through matching the knowledge in the academic environment with the competence needs of the business sector, employees can develop the competence companies need to retain and strengthen their renewal and their competitiveness.

Linnaeus University will seek support for an industrial graduate school in the field of big data, a profiled research and competence area of the two hosting universities. [WHICH ONES?]

What is big data?

The ubiquity of sensor, computing, communication and storage technologies provides us with access to previously unknown amounts of data – big data. Converted into actionable knowledge, data has become an unprecedented economic value as witnessed by the success of companies like Google, Facebook, and Twitter.

Big data has also started to revolutionise research communities and their scientific methodologies. It has innovated the approaches to knowledge and theory building, validation, and exploitation taken in science and engineering. In engineering, data based decision support and prediction models allow generating systems from data that earlier needed to be implemented manually, if possible at all. Technologies include statistics and machine learning as well as parallel and stream processing, data transformation, information visualisation, and information security.

What's in it for your company?

This is a way for you and your company to build expert competence among your own employees or, together with the universities, find new employees with competence in data analytics, digitalisation and related areas that are important for your innovation roadmap.

Together with your experts, the doctoral students will perform R&D addressing the company's questions and challenges in collecting and making accessible data sources and transforming data into actionable knowledge and value. Additionally, you get access to senior researchers from the universities helping the R&D projects and, this way, to national and international networks of leading experts in the field of big data and digitalisation.

What's expected from you company?

You invest the time of one or more of your employees to study on a doctoral programme. Your company, the university and the Knowledge Foundation jointly cover the salary of this doctotal student. You provide the doctoral student with a mentor from your company and give input to research questions of interest, and provide data and your company's expertise. The Knowledge Foundation and the university will cover all costs for the research school itself in the form of developing courses, providing senior researchers as supervisors (20-40 % of full-time) and other costs directly related with the doctoral education.

If you don’t have a person in your organisation that would be suitable and interested in joining the programme, we can jointly look for a qualified doctoral student that matches what you are looking for.

The regular study period is about five years, but the actual study time can differ between individuals. Up to 80 % of the work of the doctoral student is spent directly in your company, so you will get and hold a close contact with the doctoral student.

Details are given below:

  • 20 % of the time can be spent on regular tasks in your company.
  • 20 % course work at the universities.
  • 60 % of the time is dedicated to R&D in the research project.

Time plan

On 12 September, we will send in a proposal to the Knowledge Foundation. We need to have at least six doctoral students confirmed by companies with letters of intent, see below.

Are you interested?

Then we need the following information from you and. We will help you with the content and formulations.

  • Letter of intent: A letter that clearly shows your intent to become a part of the industry graduate school and your financial commitment, a short description of your company, the focus area of the research project, and one paragraph on your road-map and how the doctoral project could contribute to the milestones/goals in this road-map. Mind the synergy between these goals and (big) data technologies. Enclose the logotype, the contact person and a signature.
  • Project description: The research project plan (not more than 1/2 A4 page), including questions and challenges and the connection to the milestones/goals in the road-map. If possible, describe the data sources available and the approaches that you favor.

For more information, support to formulate your ideas or discussions on how to set this up, please contact the industrial graduate school's coordinator Diana Unander or leader professor Welf Löwe, no later than 30 August.