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collaboration with the Interfaces and Correlated Electronics (ICE) group at the University of Twente and the Infomatter group at AMOLF. The SMIP project aims at revolutionising computing by developing adaptive
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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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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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in the Power Electronics Group at UT and collaborate with experts from Seine Batteriesysteme, NRG2Fly, and multiple airports. The project also supports open-source developments in MW-charging protocols
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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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be part of a collaborative and inclusive team of around 35 scientists, where personal growth, professional development, and a strong sense of community are highly valued. You will contribute
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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
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, allows for flexible sampling plans. Prof. Jelle Goeman co-developed the closed testing framework that gives flexibility for defining research questions in a data-dependent way. Thirdly, we add permutation
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of the procedure and improve clinical outcomes, the goal of this research project is to develop methods for modeling and controlling a range soft/flexible surgical devices and medical microrobots, as they interact
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case study, but also on how to include different partners in the project and the development of a framework for obtaining a robust view of a health innovations full benefits to patients. PhD project 2