Connectionists: PhD Positions in Neuro-inspired Computing at Orebro University (Sweden)

[Denis Kleyko]

The Machine Perception and Interaction Lab invites applications for up to two PhD positions in Neuro-inspired Computing at the Department of Computer Science, Orebro University (Sweden). The project will focus on developing novel neuro-inspired algorithms and their hardware realizations that can make future intelligent systems more efficient and powerful.

The successful candidate should demonstrate strong independent problem-solving skills and the ability to think critically. Good communication and collaboration skills are also required. Courses, a thesis, or publications in any of the following areas will be considered a merit: digital signal processing, electrical engineering, computer vision, machine learning, artificial intelligence, cognitive science, neuromorphic computing, or robotics.

The position offers competitive working conditions, and a generous travel budget is available.

University of Registration: Orebro University (Sweden)

Application deadline: January 16, 2026.

To apply, follow the link: https://www.oru.se/english/career/available-positions/job/?jid=20250376

Project Outline: 

Today’s intelligent systems rely on massive datasets and large-scale computing infrastructure, which limits who can use them and where they can be deployed. In contrast, biological brains achieve remarkable intelligence while running on only a few watts of power. This project draws inspiration from these biological principles to rethink how intelligent systems represent information, perform computations, and physically implement their algorithms. A key research direction will be the exploration of high-dimensional neural representations that can support continual learning and reasoning. These representations are compatible with emerging computing hardware such as neuromorphic chips and in-memory computing devices. By integrating these ideas, the project aims to expand the algorithmic capabilities of intelligent systems, reduce computational costs, and tightly bridge the gap between software and hardware design.

As a doctoral student, you will work on developing a framework that connects new learning algorithms with their physical implementation, targeting emerging computing hardware. You will also evaluate these ideas in real-world domains such as signal processing and combinatorial optimization, where efficient solutions can have major societal and industrial impact. This project offers an opportunity to work at the frontier of artificial intelligence, computational neuroscience, neuro-inspired computing, and emerging hardware technologies. If you are excited by fundamental research with the potential to rethink how intelligent systems are built, this project is for you.
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