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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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candidate has an MSc in Computer Science and is interested in theory, development, or usage of formal methods. Fluency in English is required. Hosting environment FORM is embedded in the Section of Artificial
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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