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at different timescales, exploring trade-offs between different design choices, quantizing the models, and implementing the best-performing ones in custom digital hardware, on FPGA and/or in an application
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deploying the face de‑identification pipeline on resource‑constrained hardware, optimizing the underlying AI models based on latency, memory, and power constraints, and building demonstrators that showcase
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of incoherent light and use these models for the joint optimisation of the optics and computational imaging. You will also contribute to the development and prototyping of the camera optics and hardware, with
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Experience in simulating fluid mechanics problems using, e.g., COMSOL Experience in interdisciplinary research Experience in designing and building hardware solutions, e.g. using Arduino, Raspberry Pi, 3D
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. Knowledge of data analysis, optimization, and machine learning techniques is a plus. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP) and software (e.g
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. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP), and software (e.g., GNURadio, MATLAB, LabView) is a plus. You are a team player and have strong communication
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radically new platform of analog hardware accelerators — so-called Ising machines — that can efficiently speed up these computationally difficult tasks, unlike any current digital computer. These Ising