PhD Students

The fusion of competences from the IPR, FEC and ES environments in INDTECH forms the basis for building successful cross-organizational and interdisciplinary education programmes at MDU, such as the Applied AI programme, which has a strong emphasis on industrial and societal applicability. The strategic plan of MDH further commits to ensuring that research results are integrated into the educational curriculums at all levels.


All faculty in INDTECH will be active in teaching as well as engage with students to inspire and build bridges between education and research. We will also involve all industrial PhD students as guest lecturers in our courses, which will contribute to find suitable student projects at the hosting companies. We also plan to mirror research results in the MITC lab environment to promote the practical training of students.


The university’s goals with INDTECH is that within the time frame of the school, all enrolled PhD students have graduated with a PhD degree.

In addition, the INDTECH graduate school have the following high-level objectives for the students:

  • Enable higher career opportunities for INDTECH students through industrially relevant education and research, as well as awareness of opportunities in industry and academia.
  • Provide opportunities for graduate students to work in both academic and industrial environments, gaining knowledge and experience valuable for a future career.
  • Establish long-term national and international contacts and networks with leading universities/institutes, with other companies, and with other PhD students.


All PhD students will have an industrial mentor, and for several of the students, the industrial mentor also fulfills the requirements to become a co-supervisor in the doctoral education. PhD students, supervisors, and company mentors will meet regularly to ensure a tight collaboration and real co-production for each student project in INDTECH.

I have a master’s degree from Mälardalen University in product and process development with a subsequent professional career, after graduation, I worked as a consultant at one of the country’s leading consulting companies before I got a job at Volvo, one of MITC’s partner companies. My career cross academia and companies. I have been longing to return to the university and the environment with the community and varied projects, and cross-border collaborations. IndTech offers me a great opportunity to deepen my knowledge in this area.

San Giliyana Industrial PhD Student MITCH

Our Students

The co-production in INDTECH and INDTECH + between MDU and the 12 industrial partners will result in 18 graduated PhD students in a strategically important area. PhD graduates of INDTECH with a new interdisciplinary perspective will pilar digitalization and Industry 4.0 of the whole society.


San Giliyana

Smart Maintenance Technologies for the Manufacturing Industry


Adrián Sánchez de Ocaña

Digital Twins for Production Development


Alireza Dehlaghi Ghadim

Cybersecurity of Industrial Control Systems


Antonia Antoniadou

Investment casting manufacturing


Philip Wickberg

Fleet management of autonomous machines.


Pontus Netzell

Optimal control and design of future energy systems


Samaneh Mohammadi

Balancing Privacy and Performance in Emerging Applications of Federated Learning


Sania Partovian

Smart-troubleshooting in Industry 4.0


Sarmad Bashir

AI-Driven Optimization for Requirements Engineering in Railway Industry


Sirisha Bai Govardhan Rao

A Systematic Investigation on Common Cause Failures of Electro-dynamic braking system


Tim Andersson

AI Approach to Replace the Human Tactile Sense for Quality Control of Locks


Tobias Englund

Long-term production development


Vésteinn Sigurjónsson

Implementation of digitalized tools and methods in product development and industrialization projects


Wilhelm Söderkvist Vermelin

Physics and applied mathematics


Akshay Goyal

Digitalization of Industrial Production Systems

IndTech student profile

Abdulkarim Habbab

Optimizing Off-Roads Applications with Digital Twins


Nicolas Leberruyer

Implementations of AI solutions in operations


Torbjörn Trosten

Energy-efficient control of the traction drive used in trains