IRS TransTech ‑ International Research School in Transformative Technology

The International Research School in Transformative Technology is an important element of our strategy of building strong international networks for the Research Environment for Transformative Technologies and the research centres STC, FSCN and STRC at Mid Sweden University (MIUN). We expect that the sharing of PhD students will further deepen the collaborations that we have initiated through guest professors with selected strategic partner academic institutions.

IRS TransTech will challenge participating companies to adopt to transformative R&D methods and process technologies for improved efficiency, faster development, and more agile operations.  The common theme of the research school is selected transformative technologies for the business processes from research to production process to product development. Our expectation is that companies can accelerate innovations with the help of these technologies.

The aim with the research school is that all the participants, universities, and companies alike, learn and benefit from this transformation and promote it in their operations. The research in the PhD projects addresses following transformative research questions:

• Embedded AI in IoT Systems - Where and how is the optimum implementation of CNN models in an IoT context with respect to latency/throughput, node energy and system complexity?
• Triboelectric particle filtration - How should the efficient triboelectric filter material be designed to provide superior particle retention at lowest possible pressure drop? 
• AI based measurement - How should deep-learning methods be integrated into measurement systems and at the same time be a driver for improved measurement of timber and enable traceability of timber logs?
• Long-term vibration energy harvesting applications - How can existing electromagnetic harvester be designed with focus on long-term reliability?
• Intelligent and predictable self-managed wireless IoT networks - How can predictable and consistent network performance be achieved with actionable intelligence, that also respond dynamically to changes in network conditions and user demand?
• On-device continual learning for low-power embedded systems - How to develop methods for the implementation of continual learning based on time-series data in embedded systems?
• Emission Free Pulping with Ionic Liquids and Mechanical Refining - How to develop chemical process simulation tools for a radically new mild pulping concept based on ionic liquids (IL) and mechanical refining? 
• Light-Weight Intrusion Detection System for Low-Power Devices - How can light-weight Artificial Intelligence/Machine Learning (AI/ML)-based IDS solutions be used to improve the design and evolution of security-sensitive devices?

 These eight PhD projects are the core of the research, IRS TransTech will however have a cross PhD project approach to monitor and analyse the projects for their potential impact on the business and new product development process from the business sector perspective.