The industry graduate school Data Intensive Applications (DIA)

Data Intensive Applications (DIA) is a graduate school for industrial doctoral students that focuses on applied research, addressing the big data and artificial intelligence challenges of our industry partners. The industry graduate school is funded by the Knowledge foundation, Linnaeus University and the participating companies.

Wanted: Industrial doctoral students with a focus on data-intensive applications

The industry graduate school Data Intensive Applications (DIA) is recruiting new industrial doctoral students for our partner companies. The posts are permeated by data-intensive methods such as artificial intelligence and machine learning.

Electrolux Professional: Data intensive applications

Are you passionate about the intersection of big data and artificial intelligence in a real-world project and willing to collaborate with an industrial partner?

At Electrolux Professional, we strongly believe that the value can be generated by data and that the advanced usage of AI solution combined with data exploratory can help us better serve our customers and create better products.

In the role as a doctoral student, you will collaborate closely with our business leaders and other key players, to comprehend company objectives and identify data-driven strategies for achieving those objectives.

As a industrial doctoral student at Electrolux Professional, your job is to gather a large amount of data, analyze it, sort out the essential information, and then utilize advanced programming languages like R or Python to extract insights that may be used to increase the productivity and efficiency of the business. 

More information about the position and how to apply (pdf file)

Fortnox: Data intensive applications

We are now looking for an industrial PhD student in data intensive applications who wants to be involved in developing new products and services within Fortnox. Your duties will include everything from data analysis, data processing and development of AI models to identifying improvement potential and implementing solutions that are efficient, qualitative and sustainable. Fortnox's participation in the DIA Industrial Graduate School will revolve around the following main areas:

Process large amounts of data, transform them into insights and solutions that help our customers work efficiently, make work more fun and easier to do.
Using and combining data from multiple sources, which provides added value to our customers.
Reinforcement of Fortnox's competence in AI, machine learning and data analysis.

More information and application

The industry graduate school DIA

The industry graduate school Data Intensive Applications (DIA) applies academic research to industry challenges. The objective is to develop new knowledge, smarter solutions and innovations in data intensive applications, leveraging on big data, artificial intelligence (AI), and cyber-physical system (CPS) technologies.

To meet these challenges, DIA combines theoretical knowledge from computer science, mechanical engineering and forest technology with practical experience and competences. The industrial doctoral students are employed by our partner companies.

DIA contributes with structured research education and supplies companies with the fundamental competences in big data, AI and CSP, for developing smarter data intensive industry strength systems. DIA also contributes with applied research in co-production with the participating companies and across academic research fields.

The actual research is conducted in individual research projects at the participating companies. These are co-supervised by experts at Linnaeus University and at the partner companies.

The Industry Graduate School, DIA, is closely affiliated to Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) and the complete knowledge environment Smarter Systems funded by the Knowledge Foundation.

Research questions

There are three overarching research questions addressed in DIA. The individual doctoral projects, and the graduate school as a whole, will conduct research to answers these questions.

How can we utilize abstract digitalization concepts for smarter industrial CPS? How can we build, maintain, and organize open CPS ecosystems?
How can we verify guarantees of smarter industrial CPS built on data driven models regarding accuracy, performance, response time, safety, etc? How can we assure that they persist over time?
How can we make data driven models that control smarter industrial CPS self-explainable? How can we convey the knowledge from models to human stakeholders?

Industry objectives

The individual doctoral student projects address three overarching objectives, focusing on the practical relevance for the partner companies.

To adopt data intensive technologies, such as digital twins and proactive maintenance, for solving concrete problems in the development, maintenance and operation of industrial CPS, and for improving these processes.
To put together individual solutions and improvements to a common digitalization strategy towards smarter industrial CPS. To define and get started with concrete first pilot projects, set expected benefits, and define a structured systematic roadmap towards digitalization.
To create new services, platforms and ecosystems supporting smarter industrial CPS.

Industry partners and projects

DIA

Combitech: Change business logic to value-driven models
Electrolux Professional: Big data exploitation to understand professional products' life cycle
HL Design: Leveraging Machine Learning for multishop eCommerce platform
Kuka Nordic:
SKF: Machine Learning in Manufacturing
Softwerk: Advanced identification methods for the forest industry through machine learning and AI
Volvo CE: Predict and verify the products’ performance (2 doctoral students)
Virtual Manufacturing: Software as a service (SaaS), 3D modelling and twin setup and digitalization for lean production

DIA+

Volvo CE
Softwerk
Electrolux Professional
Fortnox
Micropower Group
Sigma Technology
Vultus

Courses

The courses followed by a * will be offered next academic year as well.

Study period 1 (August–November)

Code transformation and interpretation (5 credits) *

Duration: August–November
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV507-1.pdf
Coordinator: Kostiantyn Kucher
Application: Send an e-mail to kostiantyn.kucher@lnu.se
Type of course: Foundation

Data mining (5 credits) *

Duration: August–November
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV510-1.pdf
Coordinator: Rafael Messias Martins
Application: Send an e-mail to rafael.martins@lnu.se
Type of course: Data-driven

Information visualization (5 credits) *

Duration: August–November
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV805-1.pdf
Coordinator: Andreas Kerren
Application: Send an e-mail to andreas.kerren@lnu.se
Type of course: Data-driven

Project in visualization and data analysis (10 credits) *

Duration: August–January
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV807-1.pdf
Coordinator: Rafael Messias Martins
Application: Send an e-mail to rafael.martins@lnu.se
Type of course: Data-driven

Systems modeling and simulation (5 credits) *

Duration: August–November
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV650-1.pdf
Coordinator: Mauro Caporuscio
Application: Send an e-mail to mauro.caporuscio@lnu.se
Type of course: Cyber-physical systems (CPS)

Study period 2

Advanced information visualization and application (5 credits) *

Duration: November–January
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV806-1.pdf
Coordinator: Kostiantyn Kucher
Application: Send an e-mail to kostiantyn.kucher@lnu.se
Type of course: Data-driven

Formal methods (5 credits) *

Duration: November–January
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV701-1.pdf
Coordinator: Narges Khakpour
Application: Send an e-mail to narges.khakpour@lnu.se
Type of course: Foundation

Scientific methods in computer science (5 credits)
Duration: November–January
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4DV502-1.pdf
Coordinator: Aris Alissandrakis
Application: Send an e-mail to aris.alissandrakis@lnu.se
Type of course: Foundation

Structural dynamics (7.5 credits)

Duration: November–January
Syllabus: Kursplan.lnu.se/kursplaner/syllabus-4MT315-1.pdf
Coordinator: Andreas Linderholt
Application: Send an e-mail to andreas.linderholt@lnu.se
Type of course: Cyber-physical systems (CPS)

Study period 3

Computational and visual text analysis (5 credits) *
Duration: January–March
Syllabus: Syllabus-4DV808-1.pdf
Coordinator: Kostiantyn Kucher
Application: Send an e-mail to kostiantyn.kucher@lnu.se
Type of course: Data-driven

Machine learning (5 credits) *
Duration: January–March
Syllabus: Syllabus-4DV660-1.pdf
Coordinator: Welf Löwe
Application: Send an e-mail to welf.lowe@lnu.se
Type of course: Data-driven

Parallel computing (5 credits) *
Duration: January–March
Syllabus: Syllabus-4DV657-1.pdf
Coordinator: Welf Löwe
Application: Send an e-mail to welf.lowe@lnu.se
Type of course: Foundation

Project in machine learning (10 credits) *
Duration: January–June
Syllabus: https://kursplan.lnu.se/kursplaner/syllabus-4DV652-1.pdf
Coordinator: Welf Löwe
Application: Send an e-mail to welf.lowe@lnu.se
Type of course: Data-driven

Study period 4

Computational and visual network analysis (5 credits) *

Duration: March–June
Syllabus: https://kursplan.lnu.se/kursplaner/syllabus-4DV809-1.pdf
Coordinator: Kostiantyn Kucher
Application: Send an e-mail to kostiantyn.kucher@lnu.se
Type of course: Data-driven

Deep machine learning (5 credits) *

Duration: March–June
Syllabus: Syllabus-4DV661-1.pdf
Coordinator: Welf Löwe
Application: Send an e-mail to welf.lowe@lnu.se
Type of course: Data-driven

Sustainable production (7,5 credits) *

Duration: March–June
Syllabus: https://kursplan.lnu.se/kursplaner/kursplan-4MT321-1.1.pdf
Coordinator: Jetro Kenneth Pocorni
Application: Send an e-mail to Jetro Kenneth Pocorni, jetro.pocorni@lnu.se
Type of course: Cyber-physical systems (CPS)

Philosophy of Science for doctoral students (4 credits)

Duration: Maj–June
Syllabus:
Coordinator: Päivi Jokela
Application: Send an e-mail to Päivi Jokela, paivi.jokela@lnu.se
Type of course: Foundation

On demand

Using Python for research (5 credits)

Duration: Is offered continuously (individual studies)
Syllabus: https://app.box.com/s/17976bjn7o2o8s103c8ca32aqoj9ntud
Coordinator: Morgan Ericsson
Application: Send an e-mail to morgan.ericsson@lnu.se
Type of course: Foundation

Organisation

Steering committee

Responsible for the strategic governance of DIA:

Per-Olof Danielsson, Head of Virtual Product Development at Volvo Construction Equipment, chairman
Dorothee Millon, Field Quality Manager, Electrolux Professional, member
Torbjörn Danielsson, CEO and Business development, Virtual Manufacturing, member
Senadin Alisic, Strategy Advisor and Industry PhD student at Combitech Sweden, member
Åsa Blom, Vice Dean, Faculty of Technology, Linnaeus University, member
Lars Håkansson, Head of Department, Department of Mechanical Engineering, Linnaeus University, member
Niklas Malmros, CEO at Sigma Technology Solutions, co-opted member
Margrethe Hallberg, Digitalization Coordinator for Product Introductions at Scania, co-opted member

Executive board

Responsible for the operational leadership of DIA and for program and research coordination:

Welf Löwe, Professor in Computer Science, project manager
Diana Unander, Research and Project Coordinator, project coordinator
Morgan Ericsson, Associate Professor in Computer Science, program coordinator
Mauro Caporuscio, Professor in Computer Science, research coordinator

Would your company like to have an industrial doctoral student?

An industrial doctoral student is employed at a company and enrolled as a doctoral student at Linnaeus University. The student combines the regular development work with a research education and gets support from a group of senior researchers at Linnaeus University as supervisors. The research education usually stretches over five year with a set-up of 20 % course work, 60 % research and development at the company and 20 % that the company can use freely.

Do you want to know more? Contact research and project coordinator Diana Unander.