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, surgical procedures, and electrophysiology. The candidate is expected to operate independently within defined protocols and contribute to troubleshooting and optimization of experimental systems. Basic
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successful candidates will develop fundamental theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization
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performance optimization. Experimental input will be limited and used selectively for validation purposes, rather than extensive experimental campaigns, drawing on available electrochemical testing
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offers the opportunity to develop strong competencies in multi-agent systems, learning and optimization, while addressing a timely and high-impact challenge: the growing use of AI agents in
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344/2026 PhD Student (f/m/d) - Research Associate (f/m/d) for the Development of Miniaturized Ele...
Helmholtz Association of German Research Centres | Karlsruhe, Baden-Württemberg | Germany | 2 months agomeasurements that are coordinated with the electrical actuation of the electrodes. Your area of responsibility includes in particular: Development and optimization of reconfigurable, miniaturized
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computing, the approach is fundamentally different from a conceptual point of view. In this project we aim to devise and test novel approaches to solving hard optimization problems, problems which are
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, Brachytherapy, and stereotactic radiosurgery (SRS). Provides support for diagnostic imaging modalities such as X-ray, CT, MRI, PET/CT, and ultrasound. Contributes to the maintenance and optimization
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, Brachytherapy, and stereotactic radiosurgery (SRS). Provides support for diagnostic imaging modalities such as X-ray, CT, MRI, PET/CT, and ultrasound. Contributes to the maintenance and optimization
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, assistance with the design, assembly, testing, and optimization of laboratory instrumentation, and collection and analysis of experimental data. The individual will also provide support for automation and
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theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization, Position 2 on adaptive policies, and both will