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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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techniques with the constraints of silicon processes. In parallel, you will develop on-chip antenna structures, performing full-wave EM simulations and translating these designs into manufacturable layouts
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commercial electronic design libraries, combining high-frequency circuit techniques with the constraints of silicon processes. In parallel, you will develop on-chip antenna structures, performing full-wave EM
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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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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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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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with antenna-like arms to harvest the full solar spectrum (UV–Vis–IR), enabling efficient solar-driven hydrogen production while simultaneously detoxifying industrial wastewater. Join us! We invite
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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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work hands-on with clinical data and build robust deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image
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data to design robust, efficient deep learning algorithms that maximize the information extracted from images and delivered to the robot. To be successful in this role, we are looking for candidates