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for quantized and pruned neural networks, creation of quantized and pruned demonstration models, reproduction of state of the art, experiments in heterogeneous quantization Depending on expertise
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
chemistry and process engineering. We are looking for talented people to join us. In this project, neural operators are combined with classical numerical methods to enable the efficient approximation and
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to up to 30 kHz leads to the massive production of data, for which fast (even quasi-instantaneous) processing via neural networks is crucial. Objectives and expected results: The PRICELESS PhD project
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Institut de Recherche pour le Développement (IRD) | Montpellier, Languedoc Roussillon | France | 2 days ago
to confirm or challenge the initial findings. He or she will also be expected to test and develop new calibration transfer methods, including neural network approaches, notably in collaboration with
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when the shared representations between tasks are limited or trained. This project aims to test these predictions using a behavioral, neural and real-life approach. We will focus on young adults, but
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software
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communications Quantum communications Computing & Networking: QuMIMO, Quantum Error Correction, Multi-partite systems, Q Network Coding, HQCNN - Hybrid Quantum-Classical Neural Networks Security & Logic: QRL
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware
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aspects of machine learning focusing on efficiency, generalization, and sparse neural networks. Currently we are expanding our expertise by applying our theoretical findings also to robotics. Hybrid is our
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Knowledge of deep learning architectures, graph neural networks, or uncertainty quantification Familiarity with HPC environments Language Requirements: Applicants must demonstrate at least B2-level