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
-
ZHAW School of Life Sciences and Facility Management
FG Neuromorphic Computing Group
-
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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REFRACT – Repeat protein Function, Refinement, Annotation and Classification of Topologies
REFRACT is an international consortium aiming to extend our knowledge on the mechanism of tandem repeat protein (TRP) function and evolution, establishing a common classification and best practices. Starting from available state of the art computational tools and databases, it aims to drive a new level of TRP ...
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An integrated modelling and learning framework for real-time online decision assistance in Swiss agriculture
We are developing an agricultural risk decision assistant based on a unique model that can assess and visualize reliable weather and seasonal climate forecasts, soil data, and crop growth forecasts. Based on real-time and historical weather, climate, soil and crop data and novel learning algorithms, the system ...