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Research Infrastructure? No Offer Description Job description You will explore hardware/algorithm co-design for NeuroAI, simultaneously taking into account scalability inspired by modern AI workloads, as
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advantage may arise. The focus of this project is to develop techniques that will enable gate-based quantum optimization algorithms to tackle realistic (large-scale, mixed-integer, and constrained) instances
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Are you passionate about mathematical control theory and its applications? Are you eager to develop estimation and control algorithms for large-scale dynamical systems with the aim of making next
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-management algorithms for efficient and reliable long-duration operation. Implement hardware-in-the-loop (HIL) and real-time co-simulation for validation under realistic grid scenarios. Integrate battery
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of AI for the integration of multimodal healthcare data specifically incorporating patient preferences. This includes investigating new methods but also designing and benchmarking integration algorithms
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designing and benchmarking integration algorithms. The position is situated in the Intelligent Data Engineering Lab and will be supervised by Dr. Jan-Christoph Kalo and Prof. Paul Groth. What you will do
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control and energy-management algorithms for efficient and reliable long-duration operation. Implement hardware-in-the-loop (HIL) and real-time co-simulation for validation under realistic grid scenarios
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in algorithmic design; Possess good communication skills and an excellent command of English. Additional Information Benefits We encourage high responsibility and independence while collaborating with
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classical computing algorithms are NP-hard or, in general, difficult to implement. Within your application, please provide a research proposal (no more than five pages) answering the following questions: What
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strategy; participate in internal research sprints to explore, test and validate novel EO concepts, algorithms and workflows in a fast-paced, collaborative environment; engage with the innovation ecosystem