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resources, like source code, hardware design, videos, etc. A 2-minute video of yourself explaining why you are a fit for the position You may apply before obtaining your master's degree, but you cannot begin
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student will work on data processing from the digital photosensors and image reconstruction for the PET scanner. The student will be involved in the hardware work on the photosensors as well. The same
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to predict the common distortions that occur when imaging into plant roots. From here we can either correct for these distortions using the hardware in the microscope or in software using reconstruction
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computing, neuromorphic computing, and hardware acceleration for AI workloads. Key responsibilities include: Define specifications and requirements for CMOS–spintronic interfaces in collaboration with project
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spectroscopy technologies (i.e. optical hardware). • Molecular biology / protein biochemistry expertise. • Experience in analyzing protein structural data (not necessarily acquiring the data). • Having already
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: Translate ML-based error-correction / DPD algorithms into hardware-friendly forms (model reduction, sparsity, quantization, fixed-point design). Design the architecture and RTL of a low-power accelerator that
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects
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extends Graph Neural Networks (GNNs) to model distributed systems while accounting for the high heterogeneity of devices in terms of storage, processing capacities, and specific hardware characteristics
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hardware malfunctions. WP2 - Dynamic Validation Framework: This WP utilizes formal methods, specifically probabilistic model checking, to provide safety guarantees and verify AI behavior against constraints
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 3 months ago
numerous operations on relatively small dense matrices, this phase will emphasize the potential of mixed-precision strategies and hardware offloading (e.g., to GPUs or specialized accelerators) to enhance