Research Group for Neuromorphic Computing
Introduction
The Research Group for Neuromorphic Computing develops advanced neural-network based algorithms, software libraries, and systems with the new generation of computing chips – brain-inspired neuromorphic sensing and computing hardware. We focus on perception, motion planning, and control for robotic actuators with applications in life sciences: healthcare, agriculture, food processing, and smart environments. We follow a human-centered design approach to develop new generation of physical AI systems that are power-efficient, adaptive, and safe.
Expertise
- Neuromorphic computing hardware and algorithms
- Event-based vision
- Robotics: Motion planning, control, SLAM
- Efficient machine learning and AI
- Dynamical systems, cognitive architectures
Areas of application
- Assistive robotics in healthcare, agriculture, food processing, smart environments
- Machine vision in healthcare, agriculture, food processing, smart environments
- Continual learning and adaptive systems
- Robot safety, human-robot interaction
Collaborations and partners
Engagement in teaching
Our research group includes teaching engagements at BSc and MSc level as well as in continuing education.
Our Team
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ZHAW School of Life Sciences and Facility Management
FS Cognitive Computing in Life Sciences
Schloss
8820 Wädenswil -
ZHAW School of Life Sciences and Facility Management
FG Neuromorphic Computing Group
Schloss 1
8820 Wädenswil -
ZHAW School of Life Sciences and Facility Management
FG Neuromorphic Computing Group
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ZHAW School of Life Sciences and Facility Management
FS Cognitive Computing in Life Sciences
-
ZHAW School of Life Sciences and Facility Management
FG Neuromorphic Computing Group
Current projects
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Stability of self-organizing net fragments as inductive bias for next-generation deep learning
We recently released "A Theory of Natural Intelligence", proposing a possible key to the emergence of intelligence in biological learners. Goal of this fellowship is to develop a technical implementation of the concept of self-organizing netfragments within contemporary deep artificial neural nets. ...
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Investor and Stakeholder Tools for Tracking Companies’ Climate Commitments, Greenwashing and ESG Trends
In this project, we are developing science-based methods to systematically identify potential greenwashing in corporate communications as well as signals of green innovations and technologies. By applying advanced AI methods to thousands of stocks, we aim to develop novel, ESG-compliant financial products. ...