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-based, probabilistic, and in-memory computing, are based on a wide variety of physical processes, materials, architectures, and algorithms. For effective implementation, these aspects need to be mapped
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Future-Proof Smart Logistics. It aims to contribute to the realisation of the PI concept by developing advanced machine learning-based decentralised decision-making algorithms. These algorithms will enable
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this project, we aim to: Develop real-time ultrasound algorithms to estimate fascicle length in antagonistic leg muscles (tibialis anterior and soleus) in healthy individuals during walking. Translate and
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, spinal cord injury etc) through real-time neural control of wearable robotic exoskeletons. You will be developing next-generation (low and high-level) control algorithms for wearable exoskeletons that use
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operating (Waterstromen) membrane-based wastewater treatment plants. As part of the UT team, you will develop a robust model predictive control (MPC) algorithm based on sensor and other system inputs that can
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PhD position on Closed-loop testing for faster and better EM evaluation of complex high-tech systems
that you help us to solve that problem! The objectives are to develop a world-wide accepted new standard for testing large systems in RC, by comparing the newest fast EM sensor systems, comparing performance
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of stroke patients and healthy volunteers. Developing algorithms for identifying and excluding motor unit filters associated to impaired motor units. Integrating real-time-decoded features of motor unit
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of the processing system online. Our approach will be to draw on a broad selection of tools including (deep) reinforcement learning, queuing networks, online algorithms and systems engineering. In addition, a large
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verification, and proof assistants. Your focus will be on developing frameworks that pinpoint and explain the root causes of failures in interacting programs - a key step towards ensuring fault-tolerance. While