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.
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.
Publications
Current
News
- Three industrial doctoral students will present their research at AI conference News
- Strengthen your company’s development journey with an externally-employed doctoral student in AI/Computer/IT News
- Cutting-edge research centre on big data and AI granted continued funding News
- Wanted: Four industrial doctoral students with a focus on data-intensive applications News
Doctoral projects
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Doctoral project: Advanced identification methods for the forest industry through CV/AI The project intends to create opportunities for continued digitalisation in the forest industry. This concerns…
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Doctoral project: Big Data Exploitation for Insight into Electrolux Professional Products Lifecycle Management This doctoral project aims to use Big Data to map the life cycle of professional products…
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Doctoral project: Digital Twin as a Service (DTaaS) This doctoral project targets to work on Digital Twin, with integration to data intensive sources.
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Doctoral project: Digital twin developments within Volvo CE This doctoral project relates to develop a so-called digital twin platform. The aim is to understand customers' problems and support them…
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Doctoral project: Ecosystems and smart cities Cities face major climate challenges. In my research, I investigate how digital transformation and ecosystems contribute to increased collaboration…
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Doctoral project: Exploring AI driven operation for forecasting in data-light environments: The multishop concept The advent of big data and AI brought new possibilities to businesses. In this…
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Doctoral project: Machine Learning in Manufacturing Many manufacturing companies struggle with transitioning into the era of smart technologies, due to the fact that modern lab-grown methods and data…
Doctoral students
- Dag Björnberg
- dagbjornberglnuse
- Daniel Nilsson
- danielfnilssonlnuse
- Felix Viberg
- felixviberglnuse
- Gaurav Garg
- gauravgarglnuse
- Joel Cramsky
- joelcramskylnuse
- Kailash Chowdary Bodduluri
- kailashchowdaryboddulurilnuse
- Manoranjan Kumar
- manoranjankumarlnuse
- Rakhshanda Jabeen
- rakhshandajabeenlnuse
- Senadin Alisic
- senadinalisiclnuse