Research Group for Neuromorphic Computing
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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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Infectiology++ - Germ Tracking
In this project, we develop a system for the detection and analysis of germ transmission chains in the University Hospital Zurich. The system is based on an expert system solution in combination with machine learning (reinforcement learning).
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Predicting investor behaviour in European bond markets through machine learning
ICMA Quarterly Report (11.7.2019), p.23: Predicting investor behaviour in European bond markets through machine learning The quant team of ESM is developing, in cooperation with the Zurich University of Applied Sciences, a machine-learning based application to predict investor demand for syndicated bond issuances. A ...