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experimental validation of control strategies for the developed systems. This includes the development of advanced control algorithms capable of handling varying operating conditions, ensuring system stability
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and experiments with modelling of these experiments. As part of this, we have developed new algorithms and a completely new web-based platform – EasyNMR - for performing such modelling/simulations. With
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(e.g. Agentic Reinforcement Learning), evaluation, tool use, agentic harness, or retrieval-augmented systems. Internship/full-time experience from research, engineering, or algorithm-development roles in
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in e.g. programming, algorithms and data structures, software systems architecture, use of AI, data acquisition and fullstack software-development. Following the Problem-Based Learning (PBL) model, you
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integration of mechanical structures, actuators, sensors, and control hardware, development of control algorithms, and programming for embedded or robotic platforms. You are familiar with modelling and analysis
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for the quantum-classical control and readout interface, and all the way to quantum algorithms and applications. The long-term mission of the programme is to develop fault-tolerant quantum computing hardware and
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developing scalable quantum processor technologies to solutions for the quantum-classical control and readout interface, and all the way to quantum algorithms and applications. The long-term mission