Automatic Tuning of Spin-qubit Arrays

Spin-qubit quantum-dot arrays are one of the most promising candidates for universal quantum computing. However, with the size of the arrays, a bottleneck has emerged: Tuning the many control parameters of an array by hand is time-consuming and very expensive. The nascent spin-qubit industry needs a platform of algorithms that can be fine-tuned to specific sensing hardware, and which allows cold-start tuning of a device.

Verified Voting Protocols and Blockchains

There is constant interest for Internet Voting by election commissions around the world. At the same time, there is a need for online voting in blockchain governance. However, building an internet voting system is not easy: The design of new cryptographic protocols is error-prone, and public trust in the elected body is easily threatened. This project aims to improve the security and quality of the internet voting system and influence regulation on minimum quality requirements for blockchains.

Low-Code Programming of Spatial Contexts for Logistic Tasks in Mobile Robotics

Together with industrial partners, this project will investigate production scenarios where a machine can be operated by untrained personnel by using low-code development for adaptive and re-configurable robot programming of logistic tasks.

Trust through Software Independence and Program Verification

Greenland’s election law was changed in 2020, which now permits the use of Internet Voting. Together with the authorities in Greenland, this project will investigate the effects of program verification on public trust in election technologies. The project aims to contribute to making internet elections more credible, which can strengthen developing and post-conflict democracies around the world.

Multimodal Data Processing of Earth Observation Data

Based on observations of the Earth, a range of Danish public organizations build and maintain important data foundations that are used for decision-making, e.g., for executing environmental law or making planning decisions in both private and public organizations in Denmark. This project aims to support the digital acceleration of the green transition by strengthening the data foundation for environmental data.

REWORK – The future of hybrid work

Remote and hybrid work will certainly be part of most work practices, but what should these future work practices look like? Should we merely attempt to fix what we already have or can we be bolder and speculate a different kind of workplace future? Together with companies, this project seeks a vision of the future that integrates hybrid work experiences with grace and decency.

Secure Internet of Things – Risk Analysis in Design and Operation (SIoT)

This project aims to identify safety and security requirements for IoT systems and develop algorithms for quantitative risk assessment and decision-making. The aim is furthermore to create tools for designing and certifying IoT security training programs that will enable Danish companies to obtain security certification for their IoT devices, thus giving them a lead in a market that is likely to demand such certification in the near future. 

Embedded AI

AI currently relies on large data centers and centralized systems, necessitating data movement to algorithms. To address this limitation, AI is evolving towards a decentralized network of devices, bringing algorithms directly to the data. This shift, enabled by algorithmic agility and autonomous data discovery, will reduce the need for high-bandwidth connectivity and enhance data security and privacy, facilitating real-time edge learning.

HERD: Human-AI Collaboration: Engaging and Controlling Swarms of Robots and Drones

Today, robots and drones take on an increasingly broad set of tasks. However, such robots are limited in their capacity to cooperate with one another and with humans. This project aims to address multi-robot collaboration and design and evaluate technological solutions that enable users to engage and control autonomous multi-robot systems. 

EXPLAIN-ME: Learning to Collaborate via Explainable AI in Medical Education

Together with clinicians, this project aims to develop explanatory AI that can help medical staff make qualified decisions by taking the role as a mentor who provides feedback and advice for the clinicians. It is important that the explainable AI provides good explanations that are easy to understand and utilize during the medical staff’s workflow.