About the project
Objective
Develop a multiscale modelling framework that combines experimental imaging, high-resolution simulations, and machine learning to accurately and efficiently predict fluid transport in fuel cell porous layers, enhancing our understanding of the multiscale physics and enabling improved system-level design and performance optimization.
Background
The global shift toward sustainable energy requires rapid development of efficient hydrogen technologies, with fuel cells playing a central role. A major challenge is that key fuel cell components, such as porous transport layers, involve complex multiscale fluid and transport processes that are difficult to characterize and model. Advancing predictive modelling tools for these components can significantly accelerate innovation and strengthen Sweden’s emerging hydrogen value chain. This project addresses this need by developing a modern, physically grounded, data-driven modelling foundation that can support improved design of fuel cell systems and inspire broader interdisciplinary research in electrochemical energy technologies.
Crossdisciplinary collaboration
Collaboration between the FLOW group in the Engineering Mechanics department and the Applied Electrochemistry group in the Chemical Engineering department at KTH Royal Institute of Technology.
About the project
Objective
- Develop a high-fidelity simulator that replicates Swedish railway operations, including infrastructure, rolling stock, power supply, signalling, and realistic operating behaviour.
- Create and analyse operational scenarios using historical, live, or simulated data to support performance monitoring, strategic planning, and subsystem interactions.
- Design analysis and visualisation modules to evaluate railway performance, including delay propagation, energy use, track capacity, driver behaviour, and the impact of digital technologies (e.g., ATO, DAC, VC).
- Demonstrate use cases with external partners through performance analysis, disruption management, decision support, subsystem compatibility testing, and development of a scalable digital twin architecture.
Background
Railways are essential for achieving climate-neutral, energy-efficient, and resilient mobility. In Sweden, they are a key pillar of sustainable transport policy. However, increasing capacity demands, operational complexity, infrastructure ageing, and the need for digital transformation pose major challenges. Despite progress in technologies and predictive management, the sector still lacks integrated platforms for testing, real-time data use, and efficient planning.
The SPOT-Rail project addresses these gaps by developing a cross-system railway demonstrator that replicates Swedish rail operations. At its core is a high-fidelity train driving simulator connected to live and historical data, supporting research, education, and strategic planning. This enables the testing of new technologies, evaluation of operational strategies, and development of decision-support tools.
By bridging research and real-world operations, SPOT-Rail promotes safer, more efficient, and environmentally friendly rail transport. It contributes to Sweden’s and Europe’s green transition goals while fostering innovation, collaboration, and a skilled workforce for the future of sustainable mobility.
Crossdisciplinary collaboration
The project brings together expertise from multiple disciplines to enable efficient and realistic simulation of railway operations. Key fields involved include: Railway systems engineering and train operations; Human factors and interaction design; Data science and artificial intelligence; Systems engineering. These disciplines contribute various theories, simulation modelling approaches, and machine learning algorithms for predictive analysis and decision support. Furthermore, the project aims to engage students by integrating its outcomes into university courses and offering opportunities for course projects and final theses. This strengthens the link between research, education, and practical application.
About the project
Objective
This project aims to study a dramatically different approach to software development of safety-critical systems. Specifically, the overall research goal is to: develop a new foundation for agile development of complex and regulated safety-critical cyber-physical systems that enable high-confidence rapid software development of systems with certification compliance requirements.
More specifically, we will address the following research challenges by:
- designing and evaluating a new agile development methodology for software and systems design for heavily regulated domains (specifically the defense industry), posing requirements on consistency between software implementation, design documentation, and compliance control according to certification authorities.
- developing new techniques, algorithms, and methods for supporting such new agile methodology, by designing new transformer-based optimization and verification techniques that are both sound and minimize false positives.
- constructing an interactive software prototype that can, in real-time, analyze software code and documentation, automatically perform compliance checks, and report live information on a dashboard available to the R&D organization.
Sweden’s defense industry’s competitiveness is vital for the safety and security of the country, where Saab AB is the major player. Two key components for competitiveness are development speed (short lead time) and flexibility (quickly adapting to changes). Both speed and flexibility are hampered by rigid processes: this project innovates in a dramatically different approach compared to current practices.
Background
Safety-critical cyber-physical systems—such as the modern aircraft fighters like Saab Gripen—are significantly relaying on software technology. Besides strong requirements of correctness and reliability, developing such systems falls under heavy regulation and certification control, including certification standards such as DO-178C. As a consequence, the development processes of such systems are extremely complex, requiring significant manual documentation, formal meetings, and control, which result in long development, innovation, and release cycles.
On the other hand, the development of non-safety critical software has, for several decades, been using agile methodologies (e.g., Scrum and Kanban) and quick iteration cycles. Moreover, the recent trend with generative AI tools based on LLMs and transformer technology, has paved the way for even more rapid development, using AI assisted pair programming systems such as GitHub Co-pilot, CodeWhisperer, and Codeium.
The key question addressed in this project is: how can agile development methodologies and assisting software tools be designed in the context of safety-critical systems with certification and regulation requirements? Specifically, the research problems concerns (i) soundness—how can we guarantee the correctness of analyses results, (ii) completeness—how can false positives be mitigated to make the system useful in practice, and (iii) explainability—how can analysis results be traced back to source data.
Crossdisciplinary collaboration
This project is conducted in close collaboration between the aerospace and defense company Saab AB and KTH Royal Institute of Technology.
Participating in the project:
- Main PI: David Broman, Professor, KTH Royal Institute of Technology
- Co-PI: Thomas Nordh, Product Owner and Business Area Leader, Saab AB
- Co-PI: Daniel Stensér, Digital Acceleration Officer, Saab AB
About the project
Objective
This project aims to align the legal requirements for emerging digital technologies, such as AI systems, with the development of such technologies. The regulatory demands for responsible AI, such as the need for transparency, protecting privacy and taking into account fundamental rights, are mandated by the recently enacted AI Act. This project will identify and address the challenges associated with transposing the legal demands of the AI Act into technical specifications in order that the developed technology is ethical and also legally compliant from the outset.
This industrial postdoc project is a collaborative initiative between KTH, Stockholm University, and industry partner Scania. By integrating legal and technical expertise, it will contribute to Scania’s broader efforts within AI regulatory compliance and support the implementation of responsible AI practices across its internal initiatives.
Background
The ICT sector has an environmental footprint. The future development of this footprint is debated, and it Artificial intelligence (AI) has many positive uses but the widespread adoption of this technology has also raised some concerns over the risks associated with its usage, such as biased decision-making, a lack of privacy preservation, environmental concerns and the diminishing of fundamental rights in general.
The European Union AI Act entered into force on the 1st of August 2024 and is the first comprehensive legal framework regulating AI systems. Its primary objective is to promote trustworthy AI systems, thereby ensuring that AI technologies are safe, transparent and aligned with EU values, while also fostering innovation.
This project will focus primarily on AI governance with the objective of promoting responsible AI as mandated by the AI Act. It will focus on how legal demands for transparency, trustworthiness, privacy, technical robustness, and avoidance of unfair bias should be interpreted for incorporation into corporate processes so as to proactively promote legal compliance. Part of this task will involve the identification and implementation of additional soft law elements, for example, harmonized standards developed by EU standardization bodies as well as guidelines produced by various EU institutions.
Partner Postdoc
Anna De Carvalho Guimarães
Main supervisor
Tobias Oechtering, KTH
Co-supervisor(s)
Stanley Greenstein, Stockholm University
Rami Mochaourab, Scania
About the project
Objective
This project aims to propose the generalized design method and computational architecture of more than 20 kinds of nonlinear functions. By this they can be used in more algorithm acceleration without doing a lot of repetitive development work.
Background
As the foundation of the future development of smart society, the digital chips play a key role. Behind these digital chips is the hardware acceleration of a large number of algorithms. The essence of the algorithm is mathematical operation, the most complex mathematical operation is nonlinear function calculation. So the proposed research question is how to improve the universality of nonlinear function VLSI design without affecting the performance and efficiency? It is a challenge.
About the Digital Futures Postdoc Fellow
Hui Chen received a PhD from Nanjing University (NJU) in 2022, China. His major is information and communication engineering. Hui’s research interests include arithmetic circuits, integrated circuits for elementary functions and reconfigurable computing.
Main supervisor
Zhonghai Lu, Professor at the Division of Electronics and Embedded Systems, Department of Electrical Engineering, EECS, KTH.
Co-supervisor
Masoumeh Ebrahimi, Associate Professor, Division of Electronics and Embedded Systems, Department of Electrical Engineering, EECS, KTH.
About the project
Objective
This research will apply data-driven design to Climate Action by utilizing data from design, manufacturing, and the entire product lifecycle to learn how early-stage decision-making maps to downstream carbon emissions for complex systems.
Background
Artificial Intelligence has transformed the fields of Computer Vision and Natural Language Processing. Data-driven methods also have the potential to be a valuable tool in the fight against climate change, but not before such data is ready for computation. If data from product design, manufacturing, consumption, and retirement could be quantitatively represented for computation, then we could learn how to produce and consume more sustainably.
About the Digital Futures Postdoc Fellow
Haluk Akay completed his doctoral research in mechanical engineering at MIT. His doctoral thesis developed methods to represent textual design data for computation by extracting structured “what-how” information and evaluating designed systems using AI-based language modelling and design principles.
Haluk’s research interests lie in using design and data-driven methods to address complex climate change and sustainability problems. He also has experience in microelectromechanical systems (MEMS) fabrication and product design.
Main supervisor
Francesco Fuso-Nerini, Associate Professor, ITH, KTH.
Co-supervisor
Iolanda Leite, Associate Professor, Department of Robotics, Perception and Learning, KTH.
Watch the recorded presentation at the Digitalize in Stockholm 2023 event.
About the project
Objective
It indeed consists of two sub-projects. Firstly, as the most promising technology in achieving 10 Gbs peak data rates, millimetre-wave (mmWave) communications have received remarkable attention from academia and industry. Thus, in the project of intelligent wireless communications, we aim to develop several machine learning-based beam tracking algorithms for mobile mmWave communications, which can work efficiently without relying on a priori knowledge of channel dynamics. While in the project of high-accuracy positioning systems, we aim to leverage mmWave signals and other techniques, such as intelligent reflecting surfaces, to achieve centimetre-level localization accuracy.
Background
Driven by the ever-increasing mobile data traffic, 5G-and-beyond (B5G) networks are envisioned as a key enabler to support a variety of novel use cases, such as autonomous cars, industrial automation, multisensory extended reality (XR), e-health, etc. Considering the emergence of these use cases and the more and more complicated network structure, artificial intelligence is expected to be essential to assist in making the B5G version conceivable.
With regard to high-accuracy localization, it will play a critical role in almost all use cases of the B5G networks. Specifically, depending on the usage scenarios, the requirement for localization accuracy ranges from 1 cm to 10 cm for smart factory applications. However, most current localization services can, at best, achieve meter-level localization accuracy and, therefore, cannot meet the centimetre-level localization accuracy requirements of the emerging use cases in the B5G era, which emphasizes the need for more advanced localization techniques.
About the Digital Futures Postdoc Fellow
Deyou Zhang is a Digital Futures Postdoc at the School of Electrical Engineering and Computer Science of KTH, supervised by Dr Ming Xiao, Prof. Lihui Wang, and Dr Zhibo Pang. Before joining KTH, he obtained his PhD at the University of Sydney, Australia. His research interests include millimetre-wave communications, intelligent reflecting surfaces, and wireless federated learning.
Main supervisor
Ming Xiao, Associate Professor, Division of ISE, EECS School, KTH.
Co-supervisor
Zhibo Pang, Senior Principal Scientist, Department of Automation Technology, ABB Corporate Research Sweden and Adjunct Professor, Department of Intelligent Systems, EECS, KTH.
Lihui Wang, Professor and Chair of Sustainable Manufacturing, KTH.
Watch the recorded presentation at Digitalize in Stockholm 2022 event.